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.

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.

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Nutrition plays a pivotal role in depression, but dietary interventions are usually not personalized and do not consider patients microbial and human molecular functions. This preliminary study evaluated the effectiveness of precision supplements (PS) on depression symptoms as part of a personalized nutrition subscription plan that accounts for the gene expression activity of the microbiome and the human host. People with depression, 86 taking PS and 45 controls responded to the patient health questionnaire-9 (PHQ-9) at two time points an average of ~6 months apart. Categorical changes were evaluated using the PHQ-9 score system, and clinically significant categorical differences were observed between the two groups (effect size = 0.48; p <0.001). The difference in differences was calculated using multiple group propensity score weighting adjusting for age, sex, BMI, and physical activity, and the PHQ-9 score decreased by ~4 points (~29%) for the intervention group (t0: 13.75+-3.80, t1: 9.78+-6.42) vs Controls (t0: 14.07+-3.64, t1: 13.59 +-6.65). Thus, precision supplement use over ~6 months significantly reduced depression symptoms, with 69.8% of the individuals in the intervention group improving their category to no/low depression vs. 15.6% in the control group.