Nonlinear dynamics of multi-omics profiles during human aging

doi:10.1038/s43587-024-00692-2

. 2024 Aug 14.

doi: 10.1038/s43587-024-00692-2. Online ahead of print.

Nonlinear dynamics of multi-omics profiles during human aging

Xiaotao Shen^#^{1

2

3}, Chuchu Wang^#^{4

5}, Xin Zhou^{1

6}, Wenyu Zhou¹, Daniel Hornburg¹, Si Wu¹, Michael P Snyder^{7

8}

Affiliations

¹ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
² Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
³ School of Chemistry, Chemical Engineering and Biotechnology, Singapore, Singapore.
⁴ Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
⁵ Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA.
⁶ Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA.
⁷ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.
⁸ Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.

^# Contributed equally.

PMID: 39143318
DOI: 10.1038/s43587-024-00692-2

Nonlinear dynamics of multi-omics profiles during human aging

Xiaotao Shen et al. Nat Aging. 2024.

. 2024 Aug 14.

doi: 10.1038/s43587-024-00692-2. Online ahead of print.

Authors

Xiaotao Shen^#^{1

2

3}, Chuchu Wang^#^{4

5}, Xin Zhou^{1

6}, Wenyu Zhou¹, Daniel Hornburg¹, Si Wu¹, Michael P Snyder^{7

8}

Affiliations

¹ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
² Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
³ School of Chemistry, Chemical Engineering and Biotechnology, Singapore, Singapore.
⁴ Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
⁵ Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA.
⁶ Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA.
⁷ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.
⁸ Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.

^# Contributed equally.

PMID: 39143318
DOI: 10.1038/s43587-024-00692-2

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.

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References

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[1] Hou, Y. et al. Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 15, 565–581 (2019). - PubMed - DOI

[2] Hou, Y. et al. Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 15, 565–581 (2019). - PubMed - DOI

[3] Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012). - PubMed - PMC - DOI

[4] Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012). - PubMed - PMC - DOI

[5] Valdes, A. M., Glass, D. & Spector, T. D. Omics technologies and the study of human ageing. Nat. Rev. Genet. 14, 601–607 (2013). - PubMed - DOI

[6] Valdes, A. M., Glass, D. & Spector, T. D. Omics technologies and the study of human ageing. Nat. Rev. Genet. 14, 601–607 (2013). - PubMed - DOI

[7] Rutledge, J., Oh, H. & Wyss-Coray, T. Measuring biological age using omics data. Nat. Rev. Genet. 23, 715–727 (2022). - PubMed - PMC - DOI

[8] Rutledge, J., Oh, H. & Wyss-Coray, T. Measuring biological age using omics data. Nat. Rev. Genet. 23, 715–727 (2022). - PubMed - PMC - DOI

[9] Ahadi, S. et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat. Med. 26, 83–90 (2020). - PubMed - PMC - DOI

[10] Ahadi, S. et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat. Med. 26, 83–90 (2020). - PubMed - PMC - DOI

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Nonlinear dynamics of multi-omics profiles during human aging

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Nonlinear dynamics of multi-omics profiles during human aging

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