Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging

TL;DR

This study tackles the heterogeneity of human aging by constructing and validating a multi-omics aging clock from a large, deeply phenotyped cohort. By integrating transcriptomics, lipidomics, metabolomics, and microbiome data and applying nonlinear modeling, the authors reveal distinct aging archetypes (accelerated and decelerated) and two waves of molecular change across midlife, with the microbiome emerging as a central hub linking biology and disease risk. The clocks, particularly microbiome-based ones, associate with multimorbidity and disease incidence, offering a framework for personalized healthspan monitoring and preventive strategies. While promising, the findings require validation across diverse populations and longitudinal follow-up to establish causality and generalizability.

Abstract

Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological decline than chronological age. While previous studies have built aging clocks using single-omics data, they often fail to capture the full molecular complexity of human aging. In this work, we leveraged the Human Phenotype Project, a large-scale cohort of 10,000 adults aged 40-70 years, with extensive longitudinal profiling that includes clinical, behavioral, environmental, and multi-omics datasets spanning transcriptomics, lipidomics, metabolomics, and the microbiome. By employing advanced machine learning frameworks capable of modeling nonlinear biological dynamics, we developed and rigorously validated a multi-omics aging clock that robustly predicts diverse health outcomes and future disease risk. Unsupervised clustering of the integrated molecular profiles from multi-omics uncovered distinct biological subtypes of aging, revealing striking heterogeneity in aging trajectories and pinpointing pathway-specific alterations associated with different aging patterns. These findings demonstrate the power of multi-omics integration to decode the molecular landscape of aging and lay the groundwork for personalized healthspan monitoring and precision strategies to prevent age-related diseases.

Figures (5)

• Figure 1: Comprehensive data collected in an aging cohort.a, Illustration of the comprehensive clinical, physiological and behavioral data collected in the 10K study which are assigned into 12 system-level categories. b, Collection time range and sample numbers for the cohort. c, Sample collection and multi-omics data acquisition of the cohort. Three types of biological samples were collected, and 5 types of omics data were acquired. d, Spearman correlation between the first principal component and chronological ages for each type of systems. e, Heatmap depicting the dynamic changing molecules and microbes during human aging.
• Figure 2: Internal validation demonstrates the accuracy, robustness, and systemic relevance of multi-omics aging clocks.a, Pearson correlation (two-sided test) is plotted between actual chronological age and predicted age for each omics type. Linear fit is plotted as a dashed line. b, To verify the robustness of the aging clock models, the training set and internal validation set are randomly resampled, and the model is trained for 10 times. Pearson correlation (two-sided test), $r^2$, r.m.s.e., m.a.e. and med.a.e are used as evaluation criteria. c, Proportion of biomarkers significantly associated with each omics AgeAccel across different physiological systems. For each system, elements represent the percentage of all measured biomarkers within that system that are significantly correlated (FDR-adjusted p $<$ 0.05, Pearson correlation) with a given omics AgeAccel. This analysis highlights the systemic breadth of each clock. d, UpSet plot showing the number of unique and shared biomarkers significantly associated with the different aging omics AgeAccels. The horizontal bars indicate the total number of significant biomarkers for each omics AgeAccel. The upper vertical bars show the number of biomarkers in each intersection, with the matrix below indicating which omics AgeAccels are included in that intersection. This illustrates the specificity and overlap of biological signals captured by each type of omics AgeAccels.
• Figure 3: Distinct molecular aging trajectories and systemic signatures in accelerated and decelerated aging.a-d, Dynamics of significantly altered biological features across age in the accelerated (a, b) and decelerated (c, d) aging groups. a, c, Sankey diagrams showing the number of features that significantly increase or decrease with age relative to the 40–45-year baseline (two-sided Mann–Whitney U-test, FDR-adjusted P$<$0.05). b, d, Pathway enrichment of genes significantly altered between the 55–60 and 60–65-year groups. e, f, Temporal trajectory clusters of all multi-omics features for the accelerated (e) and decelerated (f) aging groups, identified using Fuzzy C-means clustering. Thin lines represent individual z-scored trajectories, and thick lines denote cluster centroids. The number of features (n) per cluster is indicated. g, Heatmap showing the percentage of features that are significantly different between the accelerated and decelerated aging groups across various physiological systems. Color intensity corresponds to the percentage of significant features within each system, identified by one-way ANOVA.
• Figure 4: Accelerated and decelerated aging exhibit distinct temporal waves of molecular change across multi-omics layers.a, The number of significantly age-associated biological features across chronological age in the accelerated aging group. Each panel corresponds to a specific omics type. The y-axis represents the count of features significantly correlated with age (FDR-adjusted q $<$ 0.05) at each age point. Analyses are presented for the entire cohort (all, orange), females only (red), and males only (blue). b, Same as a, but for the decelerated aging group. The plots reveal different magnitudes and temporal peaks of significant molecular changes compared to the accelerated aging group, particularly highlighting sex-specific differences in aging dynamics.
• Figure 5: Multi-omics aging clocks associated with age-related diseases.a, After adjusting for covariates, multi-omics aging clocks was significantly correlated with multimorbidity in the older population analyzed. b, Associations between multi-omics aging clocks and mortality and disease incidence using OR.