Viome: A company that provides personalized nutrition and wellness recommendations based on analysis of an individual’s gut microbiome.

Similar microbial communities are anticipated to exhibit analogous effects on the host organism (Gould et al., 2018).
Once a similarity measure is defined, various cluster analysis methods may be used to find sets of samples with similar microbiota.
In a single study, three robust microbiota clusters were identified using cluster analysis from 16s rRNA data of fecal samples (Arumugam et al., 2011).

The human microbiome is known as “our second genome” and contains a major influence on our health .
Although it is known because of its resilience (Lozupone et al., 2012; Relman, 2012), unlike the human genome, it has considerable plasticity hence providing ample opportunities in the look of new forms of food, medical interventions, and dietary recommendations .
Despite recent progress in microbiome research, switching from population-wide dietary recommendations to microbiome-aware recommendations isn’t yet realized.
See Table 4, for a representative summary of recent microbiome-aware diet recommendation studies.

• Price and availability of Products are at the mercy of change with no warning .
• short-chain, saturated or unsaturated) can be determined based on specific enzyme levels.
• These are personnel who need that information in order to provide, complete, testing, analysis, and reporting linked to the Services.

antibiotics or other bacteria-destroying medications—these regular interactions come to a halt.
When you subscribe, you obtain an at-home Viome kit delivered twice a year, full access to Viome’s artificial intelligence engine, and a personalized easy-to-follow plan with precise diet and nutrition recommendations delivered via an App.
Viome was born at the prestigious Los Alamos National Lab, and comes from technology originally created for national security.

• Finally, a subset of nutrigenomics is nutritional epigenomics,7 the influence of diet on changes in gene expression without changing the DNA sequence itself.
• The vision for another nutrition revolution involves microbiome-aware dietary planning and manufacturing.

It encourages whole food and lifestyle alterations to achieve better gut health.
The info Viome Life Sciences provides is for educational and informational only use.
The information is not intended to be used by the customer for just about any diagnostic purpose and is not a substitute for professional medical advice.

Your Samples and Test Data are used with your own personal Information only to the extent necessary and for the purpose of delivering the Service for you and communicating directly with you when necessary.

Then, individual gut metabolic pathways are reconstructed using online resources such as the Virtual Metabolic Human database (Noronha et al., 2018).
Finally, constraint-based reconstruction and analysis tools (Bauer et al., 2017; Baldini et al., 2018) are used to perform in silico simulations of GENREs to recognize metabolic intake requirements to secrete vital compounds of interest.
This mechanistically sound approach has been used in a few recent studies (Shoaie et al., 2015; Bauer and Thiele, 2018).
This week, the Seattle-area startup Viome Life Sciences announced a partnership with Nordstrom to sell kits offering health insights and establish you for personalized nutrition recommendations predicated on an individual’s microbial and human gene activity levels.
The Viome Health Intelligence Test, which applies to $199, provides insights around critical aging areas like gut health, immune system health, cellular health, and biological age.
To take action, this at-home kit, that may first be sold online and then at stores starting in 2022, measures patterns of gene activity in your gut microbes and blood cells.

Then a regression model is trained to predict post-meal glucose level in line with the meal’s nutritional profile, the individual’s microbiome features, and other personal information.
For every new user and meal, post-meal sugar levels are predicted by the model, and the meal with the minimum post-meal glucose level is recommended to the user.
The same methodology can be used in a later study only using microbiome features of individuals to predict post-meal glucose levels in a bread-type recommendation system (Korem et al., 2017).
When a band of users are overrepresented in the data, the predictive model is commonly biased toward their favorite items.
The next challenge is difficulty in generalizing and personalizing recommendations, particularly if feature vectors are not informative for predictions (also relevant to the “missing quantities” challenge mentioned in Table 3).