Longevity Papers

Aging is inevitable processes which play a significant role in the development of various diseases, including cardiovascular diseases, neurodegenerative disorders, and cancers. The extension of lifespan and the improvement of age-related diseases can potentially be achieved by targeting evolutionarily conserved pathways and mechanisms through pharmacological interventions. Chrysophanol (Chr), a naturally occurring anthraquinone compound primarily derived from rhubarb of the Polygonaceae family, exhibits a wide range of pharmacological activities, including anti-cancer, anti-inflammatory, and anti-bacterial effects. However, its role in regulating aging remains unclear. In this study, we discovered that Chr extends both lifespan and healthspan in Caenorhabditis elegans by activating the DAF-2/DAF-16 insulin signaling pathway. Furthermore, we observed that Chr promoted longevity in natural aging mice, doxorubicin-induced aging mice, and transgenic mice through the conserved Insulin/IGF-1 signaling pathway. Additionally, Chr also influenced senescence-associated secretory phenotypes (SASPs) and enhanced the expression of antioxidant genes, contributing to delayed aging. These findings highlight that Chr exerts anti-aging effects from C. elegans to mammals via the evolutionarily conserved Insulin/IGF-1 signaling pathway, positioning Chr as a promising candidate for the prevention and treatment of aging and age-related diseases.

Background: We present EpImAge, an explainable deep learning tool that integrates epigenetic and immunological markers to create a highly accurate, disease-sensitive biological age predictor. This novel approach bridges two key hallmarks of aging - epigenetic alterations and immunosenescence. Methods: First, epigenetic and immunologic data from the same participants was used for AI models predicting levels of 24 cytokines from blood DNA methylation. Second, open-source epigenetic data (25 thousand samples) was used for generating synthetic immunological biomarkers and training an age estimation model. Results: Using state-of-the-art deep neural networks optimized for tabular data analysis, EpImAge achieves competitive performance metrics against 33 epigenetic clock models, including an overall mean absolute error of 7 years and a Pearson correlation of 0.85 in healthy controls, while demonstrating robust sensitivity across multiple disease categories. Explainable AI revealed the contribution of each immunological feature to the age prediction. Conclusions: The sensitivity to multiple diseases due to combining immunologic and epigenetic profiles is promising for both research and clinical applications. EpImAge is released as an easy-to-use web tool that generates the age estimates and levels of immunological parameters for methylation data, with the detailed report on the contribution of input variables to the model output for each sample.

Yeast replicative aging is cell autonomous and thus a good model for mechanistic study from a dynamic systems perspective. Utilizing an engineered strain of yeast with a switchable genetic program to arrest daughter cells (without affecting mother cell divisions) and a high throughput microfluidic device, we systematically analyze the dynamic trajectories of thousands of single yeast mother cells throughout their lifespan, using fluorescent reporters that cover a range of biological processes, including some major aging hallmarks. We found that the markers of proteostasis stand out as most predictive of the lifespan of individual cells. In particular, nuclear proteasome concentration at middle age is a good predictor. We found that cell size (measured by area) grows linearly with time, and that nuclear size grows in proportion to maintain isometric scaling in young cells. As the cells become older, their nuclear size increases faster than linear and isometric size scaling breaks down. We observed that proteasome concentration in the nucleus exhibits dynamics very different from that in cytoplasm, with much more rapid decrease during aging; such dynamic behavior can be accounted for by the change of nuclear size in a simple mathematical model of transport. We hypothesize that the gradual increase of cell size and the associated nuclear size increase lead to the dilution of important nuclear factors (such as proteasome) that drives aging. We also show that perturbing proteasome changes mitochondria morphology and function, but not vice versa, potentially placing the change of proteosome upstream of the change of mitochondrial phenotypes. Our study produced large scale single cell dynamic data that can serve as a valuable resource for the aging research community to analyze the dynamics of other markers and potential causal relations between them. It is also a useful resource for building and testing physics/AI based models that identify early dynamics events predictive of lifespan and can be targets for longevity interventions.

Telomere dysfunction is a key hallmark of aging linked to numerous age--related diseases including cardiovascular disorders, pulmonary fibrosis, and metabolic syndromes. Despite decades of research yielding strong evidence linking telomere biology to aging processes, the field faces a critical bottleneck: current telomere measurement methods require specialized molecular techniques that prevent large--scale studies and clinical implementation. Here we present TLPath, a novel deep learning framework that extracts normal tissue architecture from routine histopathology (H&E) images to predict bulk--tissue telomere length. Trained on the Genotype--Tissue Expression cohort comprising >7.3 million patch images from >5,000 whole--slide images across 919 individuals, TLPath makes a remarkable discovery: the extracted morphological features spontaneously separate young, middle--aged, and elderly individuals within most tissue types--demonstrating for the first time that aging causes substantial architectural changes in tissues detectable without explicit age supervision. These extracted features can predict bulk--telomere length with significant accuracy (>0.51 in well--represented tissues), outperforming chronological age as a predictor (correlation = 0.20) and identifying age--discordant cases -- detecting both accelerated telomeres shortening in young individuals and preserved telomeres in older individuals. Mechanistic interpretation reveals that TLPath leverages established senescence morphological markers, including nuclear--to--cytoplasmic ratio and nuclear shape variation, for its predictions. We applied TLPath in ~2,800 new GTEx biopsies where concordant with known association, the predicted telomere length is shorter across most tissues from individuals with Type 1/2 diabetes. Overall, we demonstrate that aging substantially alters tissue morphology, which TLPath captures and uses to predict telomere length, enabling large--scale telomere biology studies using existing tissue archives.

Aging is driven by fundamental mechanisms like oxidative stress, telomere shortening and changes in DNA methylation, which together prepare the ground for age-related diseases. Botanical extracts, rich in bioactive phytoconstituents, represent a promising resource for developing therapies that target these mechanisms to promote healthy aging. This study explores the geroprotective potential of Monarda didyma L. extract. In vitro analyses revealed the extract's strong antioxidant activity, ability to reduce telomere shortening, and capacity to protect against DNA damage, thereby decreasing cellular senescence and improving endothelial function. The randomized, double-blind clinical trial demonstrated that daily oral supplementation with the extract significantly improved leukocyte telomere length (LTL) and stabilized DNA methylation age (DNAmAge) in the intervention group, while the placebo group experienced accelerated epigenetic aging and hypermethylation of critical age-related genes (ELOVL2 and FHL2). The intervention group also reported enhanced quality of life, particularly in the physical domain, along with improved movement and quality sleep indices detected by questionnaire and wearable sensors. These compelling findings position Monarda didyma L. extract as a powerful candidate for future geroprotective therapies, with the potential to significantly impact healthy aging.