PT  - JOURNAL ARTICLE
AU  - Vishakh Gopu
AU  - Ying Cai
AU  - Subha Krishnan
AU  - Sathyapriya Rajagopal
AU  - Francine R. Camacho
AU  - Ryan Toma
AU  - Pedro J. Torres
AU  - Momchilo Vuyisich
AU  - Ally Perlina
AU  - Guruduth Banavar
AU  - Hal Tily
TI  - An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging
AID  - 10.1101/2020.09.17.301887
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.09.17.301887
4099  - http://biorxiv.org/content/early/2020/09/19/2020.09.17.301887.short
4100  - http://biorxiv.org/content/early/2020/09/19/2020.09.17.301887.full
AB  - Accurate measurement of the biological markers of the aging process could provide an “aging clock” measuring predicted longevity and allow for the quantification of the effects of specific lifestyle choices on healthy aging. Using modern machine learning techniques, we demonstrate that chronological age can be predicted accurately from (a) the expression level of human genes in capillary blood, and (b) the expression level of microbial genes in stool samples. The latter uses the largest existing metatranscriptomic dataset, stool samples from 90,303 individuals, and is the highest-performing gut microbiome-based aging model reported to date. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to be biologically older, and people on a vegetarian diet tend to be biologically younger. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model; targeting these mechanisms can aid in development of new anti-aging therapeutic strategies.Competing Interest StatementAll authors are employees of Viome Inc, a for-profit company