Background
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.
Methods
We have developed 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 establish and maintain healthy homeostasis.
Results
We describe the application of our precision nutrition technology platform to populations with depression, anxiety, irritable bowel syndrome (IBS), and type 2 diabetes (T2D). In a blinded interventional study, we provided the study participants with precision nutritional recommendations and observed 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 that was validated against clinical measurements of HbA1c.
Conclusion
Our AI-driven precision nutrition program achieved statistically significant improvements in clinical outcomes of depression, anxiety, IBS, and type 2 diabetes. These data support the integration of precision food and supplements into the standard of care for these chronic conditions.