The global rise in metabolic disorders, such as type 2 diabetes (T2D) and obesity, demands innovative, non-invasive therapies. This project aims to develop an AI-driven closed-loop system using transcutaneous vagus nerve stimulation (tVNS) to regulate glucose metabolism via GLP-1 (glucagon-like peptide-1) modulation. By leveraging machine learning, the system dynamically adjusts tVNS parameters to optimize glucose levels.
Key objectives include:
Modeling the tVNS-GLP-1-Glucose Relationship: Developing AI models to map the pathways between tVNS, GLP-1 secretion, and glucose regulation.
Designing Adaptive Algorithms: Using advanced AI techniques to personalize tVNS stimulation for precise glucose control.
Exploring Lifestyle and Genetic Factors: Incorporating UK Biobank data to refine predictions and enable personalized therapies.
Validating Efficacy: Simulating and evaluating the system using UK Biobank data as an alternative to pharmacological treatments.
In compliance with UK Biobank’s AI guidelines, findings will be disseminated via peer-reviewed publications and conferences, ensuring adherence to ethical standards and confidentiality. Derived variables will be returned to UK Biobank, and AI models will be developed following best practices, emphasizing safety, transparency, and fairness. This project leverages UK Biobank’s extensive datasets to create scalable, personalized, and non-invasive glucose control solutions, advancing public health.