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.

Patients with diabetes may experience adverse outcomes related to their glycemic control when hospitalized. Continuous glucose monitoring systems, insulin-dosing software, enhancements to the electronic health record, and other medical technologies are now available to improve hospital care. Because of these developments, new approaches are needed to incorporate evolving treatments into routine care. With the goal of educating healthcare professionals on the most recent practices and research for managing diabetes in the hospital, Diabetes Technology Society hosted the Virtual Hospital Diabetes Meeting on April 24-25, 2020. Because of the coronavirus disease 2019 (COVID-19) pandemic, the meeting was restructured to be held virtually during the national lockdown to ensure the safety of the participants and allow them to remain at their posts treating COVID-19 patients. The meeting focused on (1) inpatient management and perioperative care, (2) diabetic ketoacidosis and hyperglycemic hyperosmolar state, (3) computer-guided insulin dosing, (4) Coronavirus Disease 2019 and diabetes, (5) technology, (6) hypoglycemia, (7) data and cybersecurity, (8) special situations, (9) glucometrics and insulinometrics, and (10) quality and safety. This meeting report contains summaries of each of the ten sessions. A virtual poster session will be presented within two months of the meeting.

In CANA-treated patients and patient sub-groups from a network of Florida hospitals, improvements in quality measures and response durability were similar to clinical trials and other real-world studies.

Recognizing and treating the early stages of type 2 diabetes (T2D) is the most cost effective way to decrease prevalence, before heart disease, renal disease, blindness, and limb amputation become inevitable. In this study, we employ high resolution gut microbiome metatranscriptomic analysis of stool samples from 53,970 individuals to identify predictive biomarkers of type 2 diabetes progression and potential for diagnosis and treatment response. The richness of the metatranscriptomic data enabled us to develop a T2D risk model to delineate individuals with glycemic dysregulation from those within normal glucose levels, with ROC-AUC of 0.83+/-0.04. This risk score can predict the probability of having insulin dysregulation before detecting high glycated hemoglobin (HbA1c), the standard-of-care marker for prediabetes and diabetes. Additionally, a machine learning model was able to distinguish novel metatranscriptomic features that segregate patients who receive metformin and are able to control their HbA1c from those who do not. These discoveries set the stage for developing multiple therapeutic avenues for prevention and treatment of T2D.

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.