Research Paper Volume 12, Issue 12 pp 12393—12409

Multi-omics network analysis reveals distinct stages in the human aging progression in epidermal tissue

Nicholas Holzscheck1,2, , Jörn Söhle1, , Boris Kristof1, , Elke Grönniger1, , Stefan Gallinat1, , Horst Wenck1, , Marc Winnefeld1, , Cassandra Falckenhayn1, *, , Lars Kaderali2, *, ,

  • 1 Front End Innovation, Beiersdorf AG, Hamburg, Germany
  • 2 Institute for Bioinformatics, University Medicine Greifswald, Greifswald, Germany
* Co-last authors

Received: February 20, 2020       Accepted: June 1, 2020       Published: June 18, 2020
How to Cite

Copyright © 2020 Holzscheck et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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.


AUC: area under the curve (receiver operating characteristic curve); HoA: Hallmark(s) of Aging; SNF: similarity network fusion.