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Evidence-based precision nutrition improves clinical outcomes by analyzing human and microbial molecular data with artificial intelligence

Janelle Connell, Ryan Toma, Cleo Ho, Nan Shen, Pedro Moura, Ying Cai, Damon Tanton, Guruduth Banavar, Momchilo Vuyisich
doi: https://doi.org/10.1101/2021.04.24.441290
Janelle Connell
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
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Ryan Toma
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
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Cleo Ho
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
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Nan Shen
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
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Pedro Moura
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
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Ying Cai
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
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Damon Tanton
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
2Endocrinology, Diabetes And Metabolism, AdventHealth, Orlando, FL, USA
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Guruduth Banavar
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
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Momchilo Vuyisich
1Viome Research Institute, Viome, Seattle, WA and New York, NY, USA
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  • For correspondence: momo@viome.com
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Abstract

Current dietary recommendations are often generalized, conflicting, and highly subjective, depending on the source biases. This results in confusion, skepticism, and frustration in the general population. As an alternative, we propose an objective, integrated, automated, algorithmic approach to diet and supplement recommendations that is powered by artificial intelligence that analyzes individualized molecular data from the gut microbiome, the human host, and their interactions. This platform enables precise, personalized, and data-driven nutritional recommendations that consist of foods and supplements, based on the individual molecular data, to support healthy homeostasis. We describe the application of this precision technology platform to populations with depression, anxiety, irritable bowel syndrome (IBS), and type 2 diabetes (T2D). We show that our precision nutritional recommendations resulted in improvements in clinical outcomes by 36% in severe cases of depression, 40% in severe cases of anxiety, 38% in severe cases of IBS, and more than 30% in the T2D risk score which was validated against clinical measurement of HbA1c. Our data support the integration of precision food and supplements into the standard of care for these chronic conditions.

Competing Interest Statement

All authors are employees of Viome, Inc., the sponsor of the research.

Footnotes

  • Misspelling of the word "depending" was corrected.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 26, 2021.
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Evidence-based precision nutrition improves clinical outcomes by analyzing human and microbial molecular data with artificial intelligence
Janelle Connell, Ryan Toma, Cleo Ho, Nan Shen, Pedro Moura, Ying Cai, Damon Tanton, Guruduth Banavar, Momchilo Vuyisich
bioRxiv 2021.04.24.441290; doi: https://doi.org/10.1101/2021.04.24.441290
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Evidence-based precision nutrition improves clinical outcomes by analyzing human and microbial molecular data with artificial intelligence
Janelle Connell, Ryan Toma, Cleo Ho, Nan Shen, Pedro Moura, Ying Cai, Damon Tanton, Guruduth Banavar, Momchilo Vuyisich
bioRxiv 2021.04.24.441290; doi: https://doi.org/10.1101/2021.04.24.441290

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