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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Type 2 Diabetes | These patients must have a definite diagnosis of type 2 diabetes mellitus (T2DM) according to the American Diabetes Association (ADA) standards |
| |
| Healthy control | These participants have normal glucose tolerance and normal cognition |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No Intervention: Observational Cohort | Other | No intervention:all participants did not receive any intervention measures throughout the study |
|
| Measure | Description | Time Frame |
|---|---|---|
| Baseline brain structural MRI scan | To calculate the volumes of whole brain and target brain regions | Within 1 week after neuropsychological tests |
| Baseline brain functional MRI scan | To evaluate Papez loop cross-scale network variation | Within 1 week after neuropsychological tests |
| Baseline brain diffusion tensor MRI scan | 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 | Within 1 week after neuropsychological tests |
| Baseline brain arterial spin labeling MRI scan | To calculate the blood perfusion in the whole brain and Papez circuit | Within 1 week after neuropsychological tests |
| Baseline neuropsychological performance | Montreal Cognitive Assessment,MoCA:It includes 11 examination items in 8 cognitive domains with a total score of 30 points | Day 1 of entry study |
| Baseline neuropsychological performance | Mini-mental State Examination,MMSE:The highest score is 30 points, with scores between 27-30 indicating normal and scores below 27 indicating cognitive impairment | Day 1 of entry study |
| Baseline neuropsychological performance |
| Measure | Description | Time Frame |
|---|---|---|
| Longitudinal changes of brain structural MRI scan | Compare the changes of the volumes of whole brain and target brain regions from baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of brain functional MRI scan |
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Inclusion Criteria:
Exclusion Criteria:
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T2DM patients will be recruited from the outpatient and inpatient units of the endocrinology department of the investigator's hospital. Healthy control will be recruited in the community
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Wenqing Xia, PHD | Contact | +8617749597285 | wen_qing_xia@126.com |
| Name | Affiliation | Role |
|---|---|---|
| Wenqing Xia, PHD | Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University | Recruiting | Nanjing | Jiangsu | 210000 | China |
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| D007333 | Insulin Resistance |
| D060825 | Cognitive Dysfunction |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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Complex Figure Test, CFT: The total score is 36 points, including position score and shape score
| Day 1 of entry study |
| Baseline neuropsychological performance | Verbalfluencytest, VFT: includes semantic fluency test, speech fluency test, and action fluency test | Day 1 of entry study |
| Baseline neuropsychological performance | Trail making testTMT:includes two parts, A and B | Day 1 of entry study |
| Baseline neuropsychological performance | Auditory Verbal Learning Test, VALT | Day 1 of entry study |
| Baseline neuropsychological performance | Digit span test,DST | Day 1 of entry study |
| Baseline neuropsychological performance | Digit Symbol Substitution Test, DSST | Day 1 of entry study |
| Baseline peripheral blood neuropathology biomarkers level | Fasting blood glucose(mmol/L) | Blood samples will be collected on day 1 of the entry study |
| Baseline peripheral blood neuropathology biomarkers level | C-peptide(nmol/l) | Blood samples will be collected on day 1 of the entry study |
| Baseline peripheral blood neuropathology biomarkers level | HbA1c(mmol/mol) | Blood samples will be collected on day 1 of the entry study |
| Baseline peripheral blood neuropathology biomarkers level | blood lipid(mmol/L) | Blood samples will be collected on day 1 of the entry study |
| Baseline peripheral blood neuropathology biomarkers level | postprandial blood glucose(mmol/L) | Blood samples will be collected on day 1 of the entry study |
| Baseline peripheral blood neuropathology biomarkers level | postprandial C-peptide(nmol/l) | Blood samples will be collected on day 1 of the entry study |
Compare the changes of the Papez circuit cross-scale network variation from baseline to each follow-up time points
| 36 months, 72 months |
| Longitudinal changes of brain diffusion tensor MRI scan | Compare the changes of the Papez circuit structural network from baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of brain arterial spin labeling MRI scan | Compare the changes of blood perfusion in the whole brain and Papez circuit from baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of neuropsychological performance | Compare the Montreal Cognitive Assessment(MoCA) change from the baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of neuropsychological performance | Compare the Mini-mental State Examination (MMSE) change from the baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of neuropsychological performance | Compare the Complex Figure Test (CFT) change from the baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of neuropsychological performance | Compare the Verbalfluencytest (VFT) change from the baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of neuropsychological performance | Compare the Trail making test (TMT) change from the baseline to each follow-up time points; includes two parts, A and B | 36 months, 72 months |
| Longitudinal changes of neuropsychological performance | Compare the Auditory Verbal Learning Test (VALT) change from the baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of neuropsychological performance | Compare the Digit span test (DST) change from the baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of neuropsychological performance | Compare the Digit Symbol Substitution Test (DSST) change from the baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of peripheral blood neuropathology biomarkers leve | Compare the fasting blood glucose(mmol/L)changes from baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of peripheral blood neuropathology biomarkers leve | Compare the C-peptide(nmol/l)changes from baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of peripheral blood neuropathology biomarkers leve | Compare the HbA1c(mmol/mol)changes from baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of peripheral blood neuropathology biomarkers leve | Compare the blood lipid(mmol/L)changes from baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of peripheral blood neuropathology biomarkers leve | Compare the postprandial blood glucose(mmol/L)changes from baseline to each follow-up time points | 36 months, 72 months |
| Longitudinal changes of peripheral blood neuropathology biomarkers leve | Compare the postprandial C-peptide(nmol/l)changes from baseline to each follow-up time points | 36 months, 72 months |
| Machine learning of multimodal MRI data | 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 | 72 months |
| D004700 | Endocrine System Diseases |
| D006946 | Hyperinsulinism |
| D003072 | Cognition Disorders |
| D019965 | Neurocognitive Disorders |
| D001523 | Mental Disorders |