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

Vishakh Gopu, Ying Cai, Subha Krishnan, Sathyapriya Rajagopal, Francine R. Camacho, Ryan Toma, Pedro J. Torres, Momchilo Vuyisich, Ally Perlina, View ORCID ProfileGuruduth Banavar, Hal Tily
doi: https://doi.org/10.1101/2020.09.17.301887
Vishakh Gopu
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Ying Cai
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Subha Krishnan
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Sathyapriya Rajagopal
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Francine R. Camacho
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Ryan Toma
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Pedro J. Torres
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Momchilo Vuyisich
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Ally Perlina
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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Guruduth Banavar
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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  • ORCID record for Guruduth Banavar
  • For correspondence: hal@viome.com guru@viome.com
Hal Tily
Viome Research Institute, Viome Inc, Bellevue / Los Alamos / New York City / San Diego, USA
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  • For correspondence: hal@viome.com guru@viome.com
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Abstract

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 Statement

All authors are employees of Viome Inc, a for-profit company

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 19, 2020.
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An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging
Vishakh Gopu, Ying Cai, Subha Krishnan, Sathyapriya Rajagopal, Francine R. Camacho, Ryan Toma, Pedro J. Torres, Momchilo Vuyisich, Ally Perlina, Guruduth Banavar, Hal Tily
bioRxiv 2020.09.17.301887; doi: https://doi.org/10.1101/2020.09.17.301887
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An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging
Vishakh Gopu, Ying Cai, Subha Krishnan, Sathyapriya Rajagopal, Francine R. Camacho, Ryan Toma, Pedro J. Torres, Momchilo Vuyisich, Ally Perlina, Guruduth Banavar, Hal Tily
bioRxiv 2020.09.17.301887; doi: https://doi.org/10.1101/2020.09.17.301887

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