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

Recruiting

• Conditions: Type 2 Diabetes; Insulin Resistance; Cognitive Impairment

• Registration Number: NCT06912321
• Lead Sponsor: Nanjing First Hospital, Nanjing Medical University

Brief Summary

This is a cross-sectional and longitudinal study to investigate the characteristic changes in Papez's circuit neural network activity and connectivity based on multimodal MRI, and through follow-up study of the interaction between the internal brain regions of Papez circuit and the function of the external neural network, a prediction model of the characteristic changes of Papez circuit neural network was constructed based on machine learning technology.

Detailed Description

T2DM patients may have multidimensional cognitive impairment, which is related to the damage of key brain regions in Papez's circuit. The purpose of this study is to establish a prediction model for the occurrence, development, and severity of cognitive impairment by using machine learning of Papez circuit neural network in T2DM patients. This will allow for early intelligent assessment with high accuracy and efficiency, and assist in clinical personalized treatment and early intervention. The research center has 1 principal investigator, 4 sub-investigators, and 1 nurse. Participants will include 200 patients with type 2 diabetes recruited from outpatient and inpatient departments. Additionally, 200 healthy controls will be recruited from the community. Each subject will undergo clinical information collection, biochemical measurements including fasting blood glucose, C-peptide, HbA1c, blood lipid, postprandial blood glucose, and postprandial C-peptide, multimodal MRI scans, and cognitive assessments at baseline and each follow-up visit. The study duration is 6 years, with a follow-up every 36 months. At the end of the study, all assessments will be performed again for all recruited subjects.

Study Timeline

• Study Start: 2024-05-01
• Study First Submitted: 2024-05-23
• Status Verified: 2025-02
• Study First Submitted QC: 2025-03-30
• Last Update Submitted: 2025-03-30
• Study First Posted: 2025-04-04
• Last Update Posted: 2025-04-04
• Primary Completion: 2030-05-01
• Study Completion: 2030-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 400

Inclusion Criteria

1. T2DM patients met the diagnostic criteria for diabetes (WHO, 1999) with a duration of 3-20 years; The control group met the criteria of fasting blood glucose < 6.1mmol/l and glycosylated hemoglobin < 5.7%;
2. right-handed, aged 45-70 years, with ≥8 years of education;
3. no contraindications to MRI scanning such as electronic and metal device implantation;
4. The visual acuity or corrected visual acuity and binaural hearing can meet the needs of the evaluation, and can cooperate to complete the examination.
5. without a history of substance abuse or dependence, evaluation is not used during the period of calm sleeping pills and antidepressants, not long-term use of drugs to improve cognitive.

Exclusion Criteria

1. patients with acute metabolic complications or a history of severe hypoglycemia;
2. severe heart, liver, lung, kidney and hematopoietic system diseases; Hyperthyroidism or hypothyroidism; Stroke, alzheimer's disease, epilepsy, Parkinson's disease and other neurological history; A history of mental illness such as depression, mania, or alcohol dependence; History of loss of consciousness due to neurological diseases or traumatic brain injury;
3. one month before the laboratory examination, with a record and surgical trauma infection;

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Baseline brain structural MRI scan	Within 1 week after neuropsychological tests	To calculate the volumes of whole brain and target brain regions
Baseline brain functional MRI scan	Within 1 week after neuropsychological tests	To evaluate Papez loop cross-scale network variation
Baseline brain diffusion tensor MRI scan	Within 1 week after neuropsychological tests	To trace and reconstruct the nerve fiber tracts between the Papez circuit related brain regions and between the circuit and the outer brain regions, and to construct a structural connection network according to the characteristics of white matter conduction pathways
Baseline brain arterial spin labeling MRI scan	Within 1 week after neuropsychological tests	To calculate the blood perfusion in the whole brain and Papez circuit
Baseline neuropsychological performance	Day 1 of entry study	Digit Symbol Substitution Test, DSST
Baseline peripheral blood neuropathology biomarkers level	Blood samples will be collected on day 1 of the entry study	postprandial C-peptide（nmol/l）

Secondary Outcome Measures

Name	Time	Method
Longitudinal changes of brain structural MRI scan	36 months, 72 months	Compare the changes of the volumes of whole brain and target brain regions from baseline to each follow-up time points
Longitudinal changes of brain functional MRI scan	36 months, 72 months	Compare the changes of the Papez circuit cross-scale network variation from baseline to each follow-up time points
Longitudinal changes of brain diffusion tensor MRI scan	36 months, 72 months	Compare the changes of the Papez circuit structural network from baseline to each follow-up time points
Longitudinal changes of brain arterial spin labeling MRI scan	36 months, 72 months	Compare the changes of blood perfusion in the whole brain and Papez circuit from baseline to each follow-up time points
Longitudinal changes of neuropsychological performance	36 months, 72 months	Compare the Digit Symbol Substitution Test (DSST) change from the baseline to each follow-up time points
Longitudinal changes of peripheral blood neuropathology biomarkers leve	36 months, 72 months	Compare the postprandial C-peptide（nmol/l）changes from baseline to each follow-up time points
Machine learning of multimodal MRI data	72 months	Multimodal MRI data for machine learning, can be in different levels of calculation and analysis and research on the characteristics of neural network, found a pattern classification and predict unknown data effectively, find out the Papez loop associated with insulin resistance characteristics of neural network

Trial Locations

Locations (1)

Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University; 🇨🇳 Nanjing, Jiangsu, China