Andrei E. Tarkhov

This study shows that old white adipose tissue can rejuvenate in young hosts through thermogenic remodeling and pathway activation, reversing biological age markers.

The paper analyzes DNA methylation and electronic health records to show aging is driven by entropy and configuration changes, with implications for age reversal.

The paper investigates epigenetic aging by comparing single-cell DNA methylation changes across aging and development, revealing stochastic patterns and introducing a new algorithm for analyzing CpG clusters.

The paper introduces causality-enriched epigenetic clocks, DamAge and AdaptAge, to track detrimental and adaptive methylation changes linked to aging, showing their association with adverse outcomes and beneficial adaptations.

This paper develops a deep learning model to identify a blood-based biomarker of aging in mice using phenotypic data, finding it correlates with aging hallmarks and predicts lifespan.

The paper identifies gastrulation in Xenopus laevis as the stage with the lowest epigenetic age and highest developmental disruption, suggesting it as the onset of aging.

The paper identifies PRC2 low-methylated regions as a universal biomarker of aging and rejuvenation, showing they account for most age-related DNA methylation gain across tissues.

The paper analyzes longitudinal blood marker data to show aging is associated with declining physiological resilience and predicts a lifespan limit around 120-150 years.

This study shows that the burden of rare protein-truncating variants negatively impacts human healthspan and lifespan in large cohorts.

The paper identifies a universal transcriptomic signature of aging in C. elegans and shows it can predict lifespan extension and drug effects.

This paper shows that the Strehler-Mildvan correlation arises from fitting degeneracy in Gompertz models, not from biological homogeneity, and demonstrates this through analysis of C. elegans survival data.