Nonlinear Molecular Dynamics Reveal Critical Transitions in Human Aging and Disease Risk

Significance 

The aging process is inherently complex, involving a multitude of physiological changes that contribute to the onset of various age-related diseases, such as cardiovascular diseases, diabetes, and neurodegenerative disorders. Current research often focuses on linear molecular changes during aging, yet these approaches fail to account for the nonlinear patterns observed in disease prevalence and mortality risks that intensify at certain age points. This gap in understanding presents a significant challenge, as it limits the ability to identify critical molecular markers and therapeutic targets for extending healthspan. To address this, the researchers conducted a comprehensive multi-omics analysis to explore the nonlinear dynamics of molecular changes during human aging. By doing so, they aimed to uncover previously unreported molecular signatures and insights into the biological pathways driving these nonlinear changes, ultimately providing a deeper understanding of aging-related disease risks. New study published in Nature Aging Journal and conducted by Xiaotao Shen, Chuchu Wang, Xin Zhou, Wenyu Zhou, Daniel Hornburg, Si Wu & led by Professor Michael  Snyder from the Stanford University School of Medicine, the researchers conducted an extensive study on a cohort of 108 participants aged between 25 and 75 years, tracking them over a median period of 1.7 years, with some followed for up to 6.8 years. They performed multi-omics profiling on various biological samples, including blood, stool, skin swabs, oral swabs, and nasal swabs, collecting data across multiple omics layers, such as transcriptomics, proteomics, metabolomics, cytokine profiling, lipidomics, and microbiome analyses. Through these comprehensive analyses, the researchers aimed to detect both linear and nonlinear molecular changes associated with aging. The findings revealed that the majority of the molecular changes occurring during aging were nonlinear. By applying statistical methods such as Spearman correlation and linear regression, the researchers identified that only a small fraction (6.6%) of molecules exhibited linear changes, while a significant portion (81.03%) displayed nonlinear alterations. This suggested that aging is not a uniform process but involves complex, dynamic shifts in molecular profiles. The authors further investigated these nonlinear changes by clustering molecules with similar temporal patterns. They identified three distinct clusters of molecular trajectories, each showing different aging patterns. One cluster exhibited stability until around 60 years of age, followed by a rapid decline, while others showed fluctuations or sharp increases around specific age points. These clusters highlighted critical periods in the aging process, such as the transition around 60 years, where substantial molecular dysregulation was observed.

In addition to clustering analysis, the researchers employed a modified DE-SWAN algorithm to detect waves of molecular changes that occur at specific chronological periods. They discovered two prominent crests of dysregulated molecules around the ages of 44 and 60 years, which were consistent across various types of omics data. These findings indicated that aging-related changes involve coordinated and systemic alterations across multiple molecular components, rather than being limited to specific omics layers. The authors also explored the functional implications of these nonlinear changes. The researchers conducted pathway enrichment analysis and identified several biological pathways that undergo nonlinear changes during aging. For example, they found that oxidative stress, GTPase activity, and histone modification pathways showed significant alterations around critical age transitions. These pathways have been previously linked to aging-related processes, such as cellular homeostasis, apoptosis, and inflammation, providing further evidence of their role in the aging process.

Moreover, the study uncovered nonlinear patterns in disease risk associated with aging. The researchers identified that markers of cardiovascular health, kidney function, and type 2 diabetes (T2D) exhibited nonlinear trajectories, with significant increases in risk occurring around the age of 60. This was exemplified by the phenylalanine metabolism pathway, which is associated with cardiac dysfunction and showed a nonlinear increase after age 60. Similarly, blood urea nitrogen and serum/plasma glucose levels, markers of kidney function and T2D, respectively, also displayed nonlinear changes, highlighting the elevated risk of these conditions in older age. Overall, the experiments conducted by the researchers provided critical insights into the nonlinear dynamics of molecular changes during aging. Their findings challenged the traditional linear models of aging and offered a more nuanced understanding of the aging process, identifying specific periods where significant molecular shifts occur and linking these changes to increased disease risks. This research has important implications for developing targeted interventions aimed at mitigating age-related diseases and extending healthspan.

The significance of this study lies in its revelation of the nonlinear nature of aging at the molecular level, challenging the conventional understanding that aging is a linear and uniform process. By identifying specific periods, particularly around the ages of 44 and 60, where substantial molecular changes occur, the research provides a deeper understanding of the aging process and its connection to age-related diseases. These findings suggest that aging is marked by critical transitions that could be key to understanding why the risk of diseases such as cardiovascular disease, type 2 diabetes, and neurodegenerative disorders increases dramatically after certain ages. The implications of Professor Michael  Snyder’s study are far-reaching. First, the identification of nonlinear changes in molecular pathways opens new avenues for the development of targeted therapies and interventions that could mitigate the effects of aging and extend health span. Understanding that aging is not a steady decline but a process with distinct molecular shifts allows for the possibility of early intervention at critical periods to prevent or delay the onset of age-related diseases. Moreover, the comprehensive multi-omics approach used in this study sets a new standard for future aging research, emphasizing the importance of integrating data from multiple biological layers to capture the complexity of aging. In terms of public health, these findings could lead to more personalized approaches to healthcare for aging populations, with interventions tailored to an individual’s specific molecular profile and the stage of aging they are in. This study also underscores the need for longitudinal data collection and analysis in aging research, as capturing the dynamics of aging over time is crucial for understanding the full scope of molecular changes that occur.

Nonlinear Molecular Dynamics Reveal Critical Transitions in Human Aging and Disease Risk - Medicine Innovates

About the author

Professor Michael Snyder, Ph.D.

Stanford University

Snyder Lab was the first to perform a large-scale functional genomics project in any organism, and has developed many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks.

Seminal findings from the Snyder laboratory include the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species.

About the author

Dr. Daniel Hornburg

Stanford University

I develop and integrate mass spectrometry-based multi omics solutions to characterize the proteome, metabolome as well as lipidome and dissect molecular big data through tailored machine-learning strategies. From 2011 to 2017, I worked with Matthias Mann (Max Planck Institute for Biochemistry) developing and applying mass spectrometry technologies to investigate the complex protein networks of our immune system as well as proteome perturbations that associate with ALS, Alzheimer’s, and Huntington’s disease. Last fall, I joined Mike Snyder (Stanford) to integrate lipidomics as a novel molecular lens into our multi omics precision medicine efforts. My mission is to discover personalized molecular fingerprints across omes opening up new avenues to define health and disease phenotypes and predict personalized disease trajectories.

Reference

Shen, X., Wang, C., Zhou, X. et al. Nonlinear dynamics of multi-omics profiles during human agingNat Aging (2024). https://doi.org/10.1038/s43587-024-00692-2

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