RT Journal Article
SR Electronic
T1 Evidence-based precision nutrition improves clinical outcomes by analyzing human and microbial molecular data with artificial intelligence
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.24.441290
DO 10.1101/2021.04.24.441290
A1 Janelle Connell
A1 Ryan Toma
A1 Cleo Ho
A1 Nan Shen
A1 Pedro Moura
A1 Ying Cai
A1 Damon Tanton
A1 Guruduth Banavar
A1 Momchilo Vuyisich
YR 2021
UL http://biorxiv.org/content/early/2021/04/26/2021.04.24.441290.1.abstract
AB 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 StatementAll authors are employees of Viome, Inc., the sponsor of the research.