Development of an epigenetic clock to predict visual age progression of human skin.

Aging is a complex process characterized by the gradual decline of physiological functions, leading to increased vulnerability to age-related diseases and reduced quality of life. Alterations in DNA methylation (DNAm) patterns have emerged as a fundamental characteristic of aged human skin, closely linked to the development of the well-known skin aging phenotype. These changes have been correlated with dysregulated gene expression and impaired tissue functionality.

Modeling transcriptomic age using knowledge-primed artificial neural networks.

The development of 'age clocks', machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design.

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

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 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.

Reduced DNA methylation patterning and transcriptional connectivity define human skin aging.

Epigenetic changes represent an attractive mechanism for understanding the phenotypic changes associated with human aging. Age-related changes in DNA methylation at the genome scale have been termed 'epigenetic drift', but the defining features of this phenomenon remain to be established. Human epidermis represents an excellent model for understanding age-related epigenetic changes because of its substantial cell-type homogeneity and its well-known age-related phenotype.