It turns out: One-size-fits-all solutions don’t actually solve much. At DayTwo we believe individuals should be treated as... individuals. We offer precision health because care can and should be as unique as your biology. Nowhere is this more evident than with diabetes. Instead of relying on traditional (but outdated) science, we should have been trusting our guts all along.
Microbiome Research Shows
Source: Personalized Nutrition by Prediction of Glycemic Responses. Zeevi et al., 2015, Cell 163, 1079–1094.
*1
Tamera
John
Gloria
Microbiome Research Shows
Source: Personalized Nutrition by Prediction of Glycemic Responses. Zeevi et al., 2015, Cell 163, 1079–1094.
*1
Tamera
John
Gloria
What DayTwo’s Precision Health Enables
This is vital for diabetes care. Our innovation allows people to normalize their blood sugar using their body’s own wisdom---without more meds or costly interventions.
Microbiome Research Shows
Tamera
John
Gloria
Each gut microbiome is made up of close to 40 trillion micro-organisms, including species of bacteria, yeast, and more. It’s a unique environment that also serves as an individual blueprint for health.
In the case of diabetes care, DayTwo’s research shows that a person’s microbiome can actually be used to predict their blood sugar response to foods— before the first bite. The impact of this is huge for people with diabetes.2
Your one-of-a-kind microbial
ecosystem forms a unique
blueprint for health.
Digestion, immunity, vitamin
production, & mental health—
your microbiome impacts it all!
The bacteria in your gut help
synthesize key hormones to
balance blood sugar.
Thanks to DayTwo’s
microbiome science + AI,
food choices become easier.
Your one-of-a-kind microbial ecosystem forms a unique
blueprint for health.
Digestion, immunity, vitamin production, & mental health—
your microbiome impacts it all!
The bacteria in your gut help synthesize key hormones to
balance blood sugar.
Thanks to DayTwo’s
microbiome science + AI,
food choices become easier.
What the Microbiome Discovery Platform Unlocks
(Know your response... before the first bite!)
Our members are seeing major health improvements across numerous measurements.
In the same way a music streaming platform can use artificial intelligence (AI) to find which exact songs you'll love, our sophisticated algorithm goes deep (by profiling your individual microbiome) and then wide (by analyzing thousands of foods combinations) to deliver truly accurate blood sugar response predictions. This means food recommendations that you–– and your biology–– will love.
85,000+
Microbiome data samples of the highest resolution
1,000,000
Continuous Glucose Monitoring data points
5,000,000
Food, grocery, and restaurant menus scored
13,000,000 & growing!
Meals logged in the DayTwo app
Utilizing new tools to pull meaning from huge datasets has the potential to drive real change in healthcare. From offering personalized food recommendations, with the potential for creating intelligent disease predictors and specialized drug development, our AI platform is just beginning to tap into the future of wellness.
We have over 10 years of peer-reviewed research with these remarkable academic partners.
2022 Research
Key Microbiome Research Shows:
Diabetes Care 2021;44(9):1980–1991
https://doi.org/10.2337/dc21-0162
The Daytwo program outperformed the traditional approach of an ADA-backed Mediterranean diet.
Summary: To compare the clinical effects of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet on glycemic control and metabolic health in prediabetes.
In this clinical trial in prediabetes, a PPT diet improved glycemic control significantly more than a MED diet as measured by daily time of glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c. These findings may have implications for dietary advice in clinical practice.
2022 Research
Key Microbiome Research Shows:
Zeevi et al., 2015, Cell 163, 1079–1094 November 19, 2015 ª2015 Elsevier Inc. http://dx.doi.org/10.1016/j.cell.2015.11.001
Our results suggest that AI-personalized diets may successfully modify blood glucose response and its metabolic consequences.
Summary: We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences.
2022 Research
Key Microbiome Research Shows:
Korem et al., 2017, Cell Metabolism 25, 1243–1253 June 6, 2017 ª 2017 Elsevier Inc. http://dx.doi.org/10.1016/j.cmet.2017.05.002
We found no significant differential effects of bread type on multiple clinical parameters. The gut microbiota composition remained person specific throughout this trial and was generally not affected by the intervention.
Summary: Bread is consumed daily by billions of people, yet evidence regarding its clinical effects is contradicting. Here, we performed a randomized crossover trial of two 1-week-long dietary interventions comprising consumption of either traditionally made sourdough leavened whole-grain bread or industrially made white bread. We found no significant differential effects of bread type on multiple clinical parameters. The gut microbiota composition remained person specific throughout this trial and was generally resilient to the intervention. We demonstrate statistically significant interpersonal variability in the glycemic response to different bread types, suggesting that the lack of phenotypic difference between the bread types stems from a person-specific effect. We further show that the type of bread that induces the lower glycemic response in each person can be predicted based solely on microbiome data prior to the intervention. Together, we present marked personalization in both bread metabolism and the gut microbiome, suggesting that understanding dietary effects requires integration of person-specific factors.
2022 Research
Key Microbiome Research Shows:
JAMA Network Open. 2019;2(2):e188102. doi:10.1001/jamanetworkopen.2018.8102
The Mayo Clinic study validated that a personalized, predictive model that considers unique features of the individual (such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content) is more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods.
Summary: Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.
2022 Research
Key Microbiome Research Shows:
Am J Clin Nutr 2019;110:63–75.
The algorithm (AI model) trained with an Israeli cohort predicts blood glucose response for a cohort of Midwestern individuals despite differences between the two populations, and outperforms common approaches used to inform dietary interventions to regulate blood sugar control.
Summary: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials.gov as NCT02945514.
2022 Research
Peer-Reviewed Microbiome Research Shows:
Nature volume 555, pages 210–215 (2018)
Microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data.
Summary: Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.
Today our robust microbiome data is being used to predict blood sugar response,
but its applications for disease prevention could reach far beyond.
Imagine a future where people can unlock the brilliance of their own biology to make lasting improvements to their health. As a precision health company, we are committed to creating individualized solutions for chronic disease that are informed by rich data rather than outdated one-size-fits-all approaches. We do this via our Microbiome Discovery Platform, an AI-powered prediction engine
Imagine a future where people can unlock the brilliance of their own biology to make lasting improvements to their health. As a precision health company, we are committed to creating individualized solutions for chronic disease that are informed by rich data rather than outdated one-size-fits-all approaches. We do this via our Microbiome Discovery Platform, an AI-powered prediction engine that is fueled by one of the world’s largest and richest datasets, with a combination of over 85,000 high-resolution microbiome samples and anthropometric inputs to date. Initially, DayTwo’s founding scientists set out to tackle the global epidemic of metabolic disease by researching the “best” diet for humans. What they discovered was that...
that is fueled by one of the world’s largest and richest datasets, with a combination of over 85,000 high-resolution microbiome samples and anthropometric inputs to date. Initially, DayTwo’s founding scientists set out to tackle the global epidemic of metabolic disease by researching the “best” diet for humans. What they discovered was that...
Take a deeper dive...High-resolution sequencing for precise analysis
We use the highest-resolution analysis to discover each person’s unique microbial diversity and
abundance.
Largest & richest dataset informs predictive AI
Truly intelligent predictions are possible with a rich dataset that is specific about bacterial gene function, while others stop at the species level.
Actionable insights create sustainable change
Data isn’t enough. Empowering indiviuals with insights from their unique biology provides a roadmap for meaningful behavior change.
Unparalleled precision is built from sequencing bacterial gene data and adding this to multiple other inputs. When we have more data points, we can create even more accurate prediction models.
Precision built with de-identified data. It matters that we have all of the inputs, not just the microbiome.
Our data goes very deep on the individual level, but also very wide across one of the largest microbiome databases in the world: Over 85,000 people and counting...
Unparalleled precision is built from sequencing bacterial gene data and adding this to multiple other inputs. When we have more data points, we can create even more accurate prediction models.
Precision built with de-identified data. It matters that we have all of the inputs, not just the microbiome.
Our data goes very deep on the individual level, but also very wide across the largest microbiome database in the world: Over 85,000 people and counting...
DayTwo’s Microbiome Discovery Platform has unparalleled versatility in training learning models that can respond to various disease states.
We employ the highest-resolution analysis to discover patterns in microbiome traits that can indicate probability of disease.
Using microbiome analysis, we can assess lacks in bacterial species and how they can be remedied using personalized probiotics.
Microbiome profiling can identify
which solutions may be the most
effective in treating certain
conditions.
We explore how machine learning
models can be trained to predict
and address disease using
microbiome data.
DayTwo’s Microbiome Discovery Platform has unparalleled versatility in training learning models that can respond to various disease states.
We employ the highest-resolution analysis to discover patterns in microbiome traits that can indicate probability of disease.
Using microbiome analysis, we can assess lacks in bacterial species
and how they can be remedied
using personalized probiotics.
Microbiome profiling may identify which solutions could be the most effective in treating certain
conditions.
We explore how machine learning models can be trained to predict
and address disease using microbiome data.
Citation and Footnotes