Study of Papez Loop Neural Network Changes and Cognitive Impairment Prediction in Adults with Type 2 Diabetes Mellitus Using Machine Learning

What this Trial Is About

This research aims to study the changes in the neural network activity and connectivity of Papezs circuit in people with type 2 diabetes mellitus T2DM. The study seeks to create a prediction model for the occurrence, development, and severity of cognitive impairment related to insulin resistance in T2DM. By using machine learning and multimodal MRI, the study will explore how key brain regions interact and affect cognitive function over time. Participants include 200 T2DM patients diagnosed according to the American Diabetes Association standards and 200 healthy controls with normal glucose tolerance and cognition. There is no intervention given during the study. All participants will undergo biochemical tests such as fasting blood glucose, C-peptide, HbA1c, blood lipids, and postprandial glucose and C-peptide. Additionally, multimodal MRI scans and cognitive assessments will be conducted at the start and during follow-up visits every 36 months, over a total study duration of 6 years. During the study, participants will have brain scans including structural, functional, diffusion tensor, and arterial spin labeling MRI, alongside neuropsychological tests and blood sample collection for neuropathology biomarkers. These assessments will be repeated at baseline and at 36 and 72 months. The study will track changes in brain imaging, cognitive performance, and biomarker levels to better understand and predict cognitive decline in T2DM. Data will be analyzed using machine learning approaches to develop predictive tools for clinical use.

Characteristics of Papez Loop Neural Network in T2DM （Type 2 Diabetes Mellitus）