Abstract
In recent years, reports of non-linear regulations in age- and longevity-associated biological processes have been accumulating. Inspired by methodological advances in precision medicine involving the integrative analysis of multi-omics data, we sought to investigate the potential of multi-omics integration to identify distinct stages in the aging progression from ex vivo human skin tissue. For this we generated transcriptome and methylome profiling data from suction blister lesions of female subjects between 21 and 76 years, which were integrated using a network fusion approach. Unsupervised cluster analysis on the combined network identified four distinct subgroupings exhibiting a significant age-association. As indicated by DNAm age analysis and Hallmark of Aging enrichment signals, the stages captured the biological aging state more clearly than a mere grouping by chronological age and could further be recovered in a longitudinal validation cohort with high stability. Characterization of the biological processes driving the phases using machine learning enabled a data-driven reconstruction of the order of Hallmark of Aging manifestation. Finally, we investigated non-linearities in the mid-life aging progression captured by the aging phases and identified a far-reaching non-linear increase in transcriptional noise in the pathway landscape in the transition from mid- to late-life.
Highlights
Biological age represents the main risk factor for most chronic human pathologies, which is why therapies slowing the aging progression and postponing the onset of age-driven disease manifestation have frequently been suggested as major interventions to improve human health span
Association analysis to subject metadata showed that the clusters were significantly associated with chronological age (p = 5.8e-12, Figure 3A), whilst not being confounded by BMI (p = 0.71, Supplementary Figure 1A)
Aging phase outliers are biological age outliers in the sense of DNA methylation (DNAm) age As subjects within the phases still presented considerable variation in chronological age, and the most proven approximation to biological age available to date is through the use of DNAm age, we further explored if the chronological outliers in the different aging phases were outliers in the sense of DNAm age
Summary
Biological age represents the main risk factor for most chronic human pathologies, which is why therapies slowing the aging progression and postponing the onset of age-driven disease manifestation have frequently been suggested as major interventions to improve human health span. The notion of aging being a continuous process remained Though, this view has been questioned by reports on non-linearity and discontinuities in biological processes associated with aging and longevity. The transition to the second aging phase, marked by decreased motor activity and heightened inflammation, www.aging-us.com was accompanied by an exponentially increased mortality risk. This 2-phased model was able to reproduce a variety of experimental longevity curves [12]. The report provides compelling evidence that discontinuous processes might be a previously overlooked feature of human aging as well and indicate that the progression of biological aging on a molecular level might be even more intricately regulated and complex than previously assumed
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