Nonlinear dynamics of multi-omics profiles during human aging

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.

Fig. 1. Most molecules and microbes undergo nonlinear changes during human aging.

a, The demographics of the 108 participants in the study are presented. b, Sample collection and multi-omics data acquisition of the cohort. Four types of biological samples were collected, and 10 types of omics data were acquired. c, Collection time range and sample numbers for each participant. The top x axis represents the collection range for each participant (read line), and the bottom x axis represents the sample number for each participant (bar plot). Bars are color-coded by omics type. d, Significantly changed molecules and microbes during aging were detected using the Spearman correlation approach (P < 0.05). The P values were not adjusted (Methods). Dots are color-coded by omics type. e, Differential expressional molecules/microbes in different age ranges compared to baseline (25–40 years old, two-sided Wilcoxon test, P < 0.05). The P values were not adjusted (Methods). f, The linear changing molecules comprised only a small part of dysregulated molecules in at least one age range. g, Heatmap depicting the nonlinear changing molecules and microbes during human aging.

Fig. 2. Clustering reveals nonlinear changes in multi-omics profiling during human aging.

a, Spearman correlation (cor) between the first principal component and ages for each type of omics data. The shaded area around the regression line represents the 95% confidence interval. b, The heatmap shows the molecular trajectories in 11 clusters during human aging. The right stacked bar plots show the percentages of different kinds of omics data, and the right box plots show the correlation distribution between features and ages (n = 108 participants). c, Three notable clusters of molecules that exhibit clear and straightforward nonlinear changes during human aging. The top stacked bar plots show the percentages of different kinds of omics data, and the top box plots show the correlation distribution between features and ages (n = 108 participants). The box plot shows the median (line), interquartile range (IQR) (box) and whiskers extending to 1.5 × IQR. Bars and lines are color-coded by omics type. Abs, absolute.

Fig. 3. Functional analysis of nonlinear changing molecules in each cluster.

a, Pathway enrichment and module analysis for each transcriptome cluster. The left panel is the heatmap for the pathways that undergo nonlinear changes across aging. The right panel is the pathway similarity network (Methods) (n = 108 participants). b, Pathway enrichment for metabolomics in each cluster. Enriched pathways and related metabolites are illustrated (Benjamini–Hochberg-adjusted P < 0.05). c, Four clinical laboratory tests that change during human aging: blood urea nitrogen, serum/plasma glucose, mean corpuscular hemoglobin and red cell distribution width (n = 108 participants). The box plot shows the median (line), interquartile range (IQR) (box) and whiskers extending to 1.5 × IQR.

Fig. 4. Waves of molecules and microbes during aging.

a, Number of molecules and microbes differentially expressed during aging. Two local crests at the ages of 44 years and 60 years were identified. b,c, The same waves were detected using different q value (b) and window (c) cutoffs. d, The number of molecules/microbes differentially expressed for different types of omics data during human aging.

Fig. 5. Functional analysis of aging-related waves of molecules across the human lifespan.

a, Pathway enrichment and biological functional module analysis for crests 1 and 2. Dots and lines are color-coded by omics type. b, The overlapping of molecules between two crests and three clusters.

Extended Data Fig. 1. Demographic data of all the participants in the study.

a, The ages positively correlate with BMI. The shaded area around the regression line represents the 95% confidence interval. b, Gender with age. c, Ethnicity with age. d, Insulin response with age. e, biological sample collection for all the participants. f, Overlap of the different kinds of omics data. g, The age range for each participant in this study.

Extended Data Fig. 2. Most of the molecules change nonlinearly during human aging.

a, Differential expressional microbes in different age ranges compared to baselines (25 – 40 years old, two-sided Wilcoxon test, p-value < 0.05). b, Most of the linear changing molecules and microbiota are also included in the molecules/microbes that significantly dysregulated at least one age range.

Extended Data Fig. 3. Omics data can represent aging.

PCA score plot of metabolomics data (a), cytokine (b), and oral microbiome (c).

Extended Data Fig. 4. Functional analysis of molecules in different clusters.

a, The Jaccard index between clusters from different datasets. b, The overlap between clusters using different types of omics data. c, Functional module detection and identification. d, Functional analysis of nonlinear changing molecules for all clusters.

Extended Data Fig. 5. Function annotation for significantly dysregulated molecules in crest 1 and 2.

a, Transcriptomics data. b, Proteomics data. c, Metabolomics data.

Extended Data Fig. 6. Pathways enrichment results for crest 1 and 2.

a, The final functional modules identified for Crest 1 and 2. b, The pathway enrichment analysis results for transcriptomics data. c, The pathway enrichment analysis results for proteomics data. d, The pathway enrichment results for metabolomics data.