An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging

摘要: Abstract Accurate measurement of the biological markers aging process could provide an “aging clock” measuring predicted longevity and allow for quantification effects specific lifestyle choices on healthy aging. Using modern machine learning techniques, we demonstrate that chronological age can be accurately from (a) expression level human genes in capillary blood, (b) microbial stool samples. The latter uses largest existing metatranscriptomic dataset, samples 90,303 individuals, is highest-performing gut microbiome-based model reported to date. Our analysis suggests associations between lifestyle/health factors, e.g., people a paleo diet or with IBS tend biologically older, vegetarian younger. We delineate key pathways systems-level decline based age-specific features our model; targeting these mechanisms aid development new anti-aging therapeutic strategies.