Paper Review: Nonlinear Dynamics of Multi-Omics Profiles During Human Aging

Overview

This longitudinal study investigates the nonlinear changes in molecular markers of aging using multi-omics profiling on a cohort of 108 participants aged 25-75 years. The research challenges the traditional linear models of aging and identifies distinct periods of significant molecular dysregulation, providing insights into the complex biological processes underlying age-related changes. The study employed both trajectory clustering and a modified DE-SWAN algorithm to analyze the data.

Key Findings

• Only a small portion of molecules exhibit linear changes with age, highlighting the importance of studying nonlinear dynamics in aging.
• Trajectory clustering revealed three distinct clusters of molecules with unique nonlinear patterns, suggesting specific age ranges with substantial molecular alterations, particularly around 60 years old.
• Two major periods of dysregulation were identified, occurring at approximately 44 years and 60 years of age, characterized by changes in immune regulation, carbohydrate metabolism, cardiovascular disease, and lipid and alcohol metabolism.
• Functional analysis linked these periods of dysregulation to specific pathways and modules, including GTPase activity, histone modification, oxidative stress, mRNA stability, autophagy, and disease risks such as cardiovascular disease, kidney issues, and type 2 diabetes.
• The modified DE-SWAN algorithm identified two prominent crests of dysregulation around the ages of 44 and 60, consistent across various omics data types, suggesting their universal nature in the aging process.

Strengths

• The study's longitudinal design allows for the tracking of individual changes over time, providing a more nuanced understanding of aging dynamics than cross-sectional studies.
• The comprehensive multi-omics approach captures a wide range of molecular changes, providing a holistic view of the aging process.
• The use of both trajectory clustering and the modified DE-SWAN algorithm provides a robust and complementary analysis of the data, strengthening the validity of the findings.
• The thorough functional analysis of the identified clusters and crests provides valuable insights into the biological processes and disease risks associated with nonlinear aging-related changes.
• The study highlights the limitations of linear models in understanding aging and emphasizes the importance of considering nonlinear dynamics.

Areas for Improvement

• Expanding the cohort size and diversity would enhance the generalizability of the findings and allow for more robust subgroup analyses.
• Incorporating data on lifestyle factors, such as diet and exercise, could provide insights into the interplay between intrinsic aging processes and extrinsic factors.
• Investigating the relationship between the identified molecular patterns and functional capacities, disease occurrences, and mortality hazards would further strengthen the clinical relevance of the findings.

Significant Elements

• Figure 1: Illustrates the nonlinear changes in molecules and microbes during human aging, showcasing the study cohort, data acquisition process, and key findings related to the nonlinearity of molecular changes.
• Figure 4: Depicts the "waves" of aging-related molecular changes, identified using a modified DE-SWAN algorithm, highlighting two prominent crests of dysregulation around the ages of 44 and 60.

Conclusion

This study provides compelling evidence for the nonlinear nature of molecular changes during human aging. The identification of distinct periods of dysregulation and their associated biological pathways offers valuable insights into the complex mechanisms underlying age-related changes and disease risks. Further research building upon these findings could pave the way for targeted interventions to promote healthy aging and mitigate age-related diseases.

Summary

This research paper investigates the nonlinear dynamics of multi-omics profiles during human aging. The study involved a longitudinal cohort of 108 participants aged between 25 and 75 years, residing in California, USA. Participants were tracked for a median period of 1.7 years, with a maximum follow-up duration of 6.8 years. Comprehensive multi-omics profiling was performed, revealing consistent nonlinear patterns in molecular markers of aging. Two major periods of substantial dysregulation were identified, occurring at approximately 44 years and 60 years of age. Distinct molecules and functional pathways associated with these periods were identified, including immune regulation and carbohydrate metabolism changes at the 60-year transition and cardiovascular disease, lipid and alcohol metabolism changes at the 40-year transition. The research concludes 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.

Strengths

• The abstract effectively summarizes the key aspects of the research, including the study design, methodology, key findings, and implications.

  'In this study, we performed comprehensive multi-omics profiling on a longitudinal human cohort of 108 participants, aged between 25 years and 75 years.'p. 1

• The abstract highlights the novelty of the study by emphasizing the focus on nonlinear changes in molecular markers of aging, which contrasts with previous research that primarily explored linear changes.

  '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.'p. 1

• The abstract clearly states the main conclusion of the study, which is that functions and risks of aging-related diseases change nonlinearly across the human lifespan.

  '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.'p. 1

Suggestions for Improvement

• While the abstract mentions two major periods of dysregulation, it could briefly elaborate on the specific types of molecules and pathways identified for each period. This would provide a more comprehensive overview of the study's findings.

• The abstract could briefly mention the potential implications of the findings for disease diagnosis, prevention, and treatment. This would enhance the translational relevance of the research.

Summary

This introductory section establishes the context for the research by highlighting the limitations of previous studies that primarily focused on linear changes in molecular markers during aging. The authors emphasize the importance of studying nonlinear changes, as the prevalence of aging-related diseases and mortality risk accelerates after specific time points, suggesting a more complex dynamic. The introduction then outlines the study's objectives, which are to perform comprehensive multi-omics profiling on a longitudinal human cohort to investigate the nonlinear dynamics of molecular markers of aging and identify distinct periods of substantial dysregulation and associated biological pathways. The authors aim to provide insights into the molecular and biological mechanisms underlying these nonlinear changes, ultimately contributing to a more comprehensive understanding of the aging process and its implications for age-related diseases.

Strengths

• The introduction effectively highlights the gap in current knowledge by emphasizing the limitations of previous research that primarily focused on linear changes in molecular markers during aging.

  '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.'p. 1

• The introduction clearly articulates the rationale for the study by emphasizing the importance of understanding nonlinear changes in molecular markers of aging to gain insights into the accelerated risk of age-related diseases at specific time points.

  'It is widely recognized that the occurrence of aging-related diseases does not follow a proportional increase with age. Instead, the risk of these diseases accelerates at specific points throughout the human lifespan6.'p. 2

• The introduction provides a concise overview of the study design and methodology, outlining the use of comprehensive multi-omics profiling on a longitudinal human cohort to investigate nonlinear changes in molecular markers of aging.

  'In this study, we performed comprehensive multi-omics profiling on a longitudinal human cohort of 108 participants, aged between 25 years and 75 years.'p. 1

Suggestions for Improvement

• The introduction could benefit from a more detailed discussion of the specific types of nonlinear changes that have been observed in previous studies, providing concrete examples of how these changes manifest in different molecular markers.

• The introduction could briefly mention the potential implications of the study's findings for disease diagnosis, prevention, and treatment. This would enhance the translational relevance of the research and highlight its potential impact on healthcare.

• The introduction could elaborate on the specific challenges and opportunities associated with studying nonlinear changes in molecular markers of aging. This would provide a more nuanced perspective on the complexities of the research and the potential for future advancements in the field.

Summary

This section presents the results of the study, starting with a description of the cohort and the multi-omics data collected. The authors highlight the nonlinear nature of molecular changes during aging, revealing that only a small portion of molecules exhibit linear changes. They employ clustering analysis to group molecules with similar trajectories, identifying three distinct clusters with clear nonlinear patterns. The authors then perform functional analysis on these clusters, uncovering pathways and modules associated with GTPase activity, histone modification, oxidative stress, mRNA stability, autophagy, and disease risks such as cardiovascular disease, kidney issues, and type 2 diabetes. They further investigate the dynamics of aging-related molecular changes using a modified DE-SWAN algorithm, identifying two prominent crests of dysregulation around the ages of 45 and 65. Functional analysis of these crests reveals modules related to CVD, skin and muscle stability, lipid and alcohol metabolism, immune dysfunction, kidney function, and carbohydrate metabolism. The authors emphasize the complementary nature of the two approaches (trajectory clustering and DE-SWAN) in capturing the nonlinear changes in molecules and functions during human aging.

Strengths

• The results section effectively presents the extensive multi-omics dataset collected, providing a detailed overview of the cohort characteristics, sample collection, and data acquisition process.

  'We collected longitudinal biological samples from 108 participants over several years, with a median tracking period of 1.7 years and a maximum period of 6.8 years, and conducted multi-omics profiling on the samples.'p. 3

• The authors convincingly demonstrate the nonlinear nature of molecular changes during aging, using both Spearman correlation and Wilcoxon tests to compare different age stages to the baseline.

  'Interestingly, using this approach, 81.03% of molecules (9,106 out of 11,305) exhibited changes in at least one age stage compared to the baseline (Fig. 1e and Extended Data Fig. 2a).'p. 3

• The clustering analysis effectively groups molecules with similar trajectories, revealing three distinct clusters with clear and compelling nonlinear patterns across the human lifespan.

  'Among the 11 identified clusters, three distinct clusters (2, 4 and 5) displayed compelling, straightforward and easily understandable patterns that spanned the entire lifespan (Fig. 2c).'p. 3

• The functional analysis of the identified clusters and crests provides valuable insights into the biological processes and disease risks associated with nonlinear aging-related changes.

  'Altogether, the comprehensive functional analysis offers valuable insights into the nonlinear changes observed in molecular profiles and their correlations with biological functions and disease risks across the human lifespan.'p. 6

Suggestions for Improvement

• While the authors acknowledge the potential influence of menopause on the transition point observed at approximately 55 years of age, they could further explore this by analyzing the data separately for pre-menopausal and post-menopausal women.

• The authors could investigate the potential impact of lifestyle factors, such as physical activity, diet, and environmental exposures, on the observed nonlinear molecular changes. This would provide a more comprehensive understanding of the interplay between intrinsic aging processes and extrinsic factors.

• The authors could explore the potential of using machine learning algorithms to predict individual aging trajectories based on the multi-omics data. This could have implications for personalized medicine and the development of targeted interventions to promote healthy aging.

Visual Elements Analysis

Summary

The discussion section delves into the implications of the study's findings, emphasizing the nonlinear nature of molecular changes during aging. The authors highlight that only a small portion of molecules exhibit linear changes, reinforcing the limitations of relying solely on linear models to understand aging. They discuss the three distinct clusters identified through trajectory clustering, pointing out specific age ranges where substantial molecular alterations occur, particularly around 60 years old. The functional analysis of these clusters is discussed, linking them to biological processes like oxidative stress, mRNA stability, autophagy, and disease risks such as cardiovascular disease, kidney issues, and type 2 diabetes. The authors further discuss the two prominent crests of dysregulation identified using the DE-SWAN algorithm, occurring around the ages of 40 and 60. They emphasize the consistency of these crests across various omics data types, suggesting their universal nature in the aging process. The functional analysis of these crests is also discussed, revealing modules associated with cardiovascular disease, skin and muscle stability, lipid and alcohol metabolism, immune dysfunction, kidney function, and carbohydrate metabolism. The authors acknowledge the study's limitations, including the modest cohort size, potential confounders related to lifestyle factors, and the focus on blood-based molecular data. They suggest future research directions, including expanding the cohort size, incorporating more diverse populations, collecting longitudinal data over longer periods, and investigating the relationship between nonlinear molecular patterns and functional capacities, disease occurrences, and mortality hazards.

Strengths

• The discussion effectively summarizes the key findings of the study, emphasizing the nonlinear nature of molecular changes during aging and the identification of specific age ranges with substantial alterations.

  'Analyzing a longitudinal multi-omics dataset involving 108 participants, we successfully captured the dynamic and nonlinear molecular changes that occur during human aging.'p. 8

• The authors thoroughly discuss the implications of the three distinct clusters identified through trajectory clustering, linking them to biological processes and disease risks, providing a comprehensive understanding of the aging process.

  'These clusters suggest that there are specific age ranges, such as around 60 years old, where distinct and extensive molecular changes occur (Fig. 2c).'p. 9

• The authors acknowledge the limitations of their study, including the modest cohort size, potential confounders related to lifestyle factors, and the focus on blood-based molecular data, demonstrating scientific rigor and transparency.

  'The present research is subject to certain constraints. We accounted for many basic characteristics (confounders) of participants in the cohort; but because this study primarily reflects between-individual differences, there may be additional confounders due to the different age distributions of the participants.'p. 10

• The discussion provides insightful suggestions for future research directions, including expanding the cohort size, incorporating more diverse populations, collecting longitudinal data over longer periods, and investigating the relationship between nonlinear molecular patterns and functional capacities, disease occurrences, and mortality hazards, paving the way for further advancements in the field.

  'In our future endeavors, the definitive confirmation of our findings hinges on determining if nonlinear molecular patterns align with nonlinear changes in functional capacities, disease occurrences and mortality hazards.'p. 10

Suggestions for Improvement

• While the discussion acknowledges the potential influence of lifestyle factors, it could delve deeper into specific lifestyle interventions, such as diet, exercise, and stress management, and their potential to modulate the observed nonlinear molecular changes.

• The discussion could explore the ethical implications of using multi-omics data for predicting individual aging trajectories and disease risks, considering potential issues related to privacy, discrimination, and access to healthcare.

• The discussion could expand on the potential translational applications of the findings, discussing how the identified molecular markers and pathways could be targeted for developing interventions to promote healthy aging and prevent age-related diseases.

Summary

The Methods section provides a detailed account of the procedures employed in the study, encompassing participant recruitment, sample collection, data acquisition, and data processing techniques. The authors meticulously outline the inclusion and exclusion criteria for participant selection, emphasizing the importance of a healthy cohort to isolate age-related changes from disease-related effects. They describe the comprehensive sample collection process, involving blood, stool, skin swab, oral swab, and nasal swab samples, highlighting the longitudinal nature of the study with multiple time points for each participant. The section then delves into the specific protocols for each omics data type, including transcriptomics, proteomics, metabolomics, cytokines, clinical laboratory tests, lipidomics, and microbiome analysis. The authors provide a detailed description of the RNA isolation, library preparation, sequencing, and data analysis steps for transcriptomics, emphasizing the use of established pipelines and quality control measures. They outline the protein extraction, fractionation, mass spectrometry analysis, and data processing steps for proteomics, highlighting the use of SWATH acquisition and statistical scoring for robust protein quantification. The section further describes the metabolite extraction, liquid chromatography separation, mass spectrometry analysis, and metabolite annotation procedures for untargeted metabolomics, emphasizing the use of both HILIC and RPLC methods for comprehensive metabolite profiling. The authors also detail the procedures for cytokine analysis, clinical laboratory tests, lipidomics, and microbiome analysis, emphasizing the use of standardized protocols and quality control measures to ensure data integrity and reliability. Finally, the section outlines the statistical methods used for data analysis, including Spearman correlation, principal component analysis, fuzzy c-means clustering, pathway enrichment analysis, and the modified DE-SWAN algorithm. The authors emphasize the use of appropriate statistical tests, multiple comparison corrections, and data normalization techniques to ensure robust and reliable results.

Strengths

• The Methods section provides a comprehensive and detailed description of the participant recruitment process, including clear inclusion and exclusion criteria, ensuring the selection of a healthy cohort for studying age-related changes.

  'Exclusion criteria encompassed conditions such as anemia, kidney disease, a history of CVD, cancer, chronic inflammation or psychiatric illnesses as well as any prior bariatric surgery or liposuction.'p. 11

• The authors meticulously outline the sample collection procedures for each biological sample type, including blood, stool, skin swab, oral swab, and nasal swab samples, highlighting the longitudinal nature of the study with multiple time points for each participant.

  'For each visit, we collected blood, stool, skin swab, oral swab and nasal swab samples.'p. 11

• The section provides a thorough description of the data acquisition and processing steps for each omics data type, including transcriptomics, proteomics, metabolomics, cytokines, clinical laboratory tests, lipidomics, and microbiome analysis, ensuring transparency and reproducibility.

  'The biological samples were used for multi-omics data acquisition (including transcriptomics from peripheral blood mononuclear cells (PBMCs), proteomics from plasma, metabolomics from plasma, cytokines from plasma, clinical laboratory tests from plasma, lipidomics from plasma, stool microbiome, skin microbiome, oral microbiome and nasal microbiome; Methods).'p. 3

• The authors clearly state the statistical methods used for data analysis, including Spearman correlation, principal component analysis, fuzzy c-means clustering, pathway enrichment analysis, and the modified DE-SWAN algorithm, demonstrating a robust analytical approach.

  'For all data processing, statistical analysis and data visualization tasks, RStudio, along with R language (v.4.2.1), was employed.'p. 12

Suggestions for Improvement

• While the authors mention adjusting for confounders such as BMI, sex, IRIS, and ethnicity, they could provide more details about the specific methods used for confounder adjustment and how these methods were chosen.

  'Before all the analyses, the confounders, such as BMI, sex, IRIS and ethnicity, were adjusted using the previously published method19.'p. 12

• The authors could elaborate on the rationale for choosing a 20-year window and 10-year parcel size in the modified DE-SWAN algorithm and discuss the potential impact of using different window and parcel sizes on the results.

  'This algorithm identifies dysregulated molecules and microbes throughout the human lifespan by analyzing molecule levels within 20-year windows and comparing two groups in 10-year parcels while sliding the window incrementally from young to old ages14.'p. 13

• The authors could discuss the limitations of using blood-based molecular data as a proxy for tissue-specific changes and suggest potential strategies for validating the findings in specific tissues, such as skin or muscle.

  'Lastly, our study's molecular data are derived exclusively from blood samples, casting doubt on its direct relevance to specific tissues, such as the skin or muscles.'p. 10

• The authors could discuss the potential impact of batch effects on the omics data and describe the specific methods used to mitigate batch effects, if any.