The Application of an AI-driven Multimodal Predictive Model for Cognitive Impairment in Patients With Type 2 Diabetes Mellitus
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 5,000
- 试验地点
- 21
- 主要终点
- Early Identification Accuracy of the AI System
研究概览
简要总结
The objective of this observational study is to evaluate the accuracy and feasibility of an artificial intelligence-based multimodal cognitive screening system in early identification of diabetes-related mild cognitive impairment (MCI) among type 2 diabetes mellitus (T2DM) patients through a 3-5 year follow-up. It also aims to analyze the correlation between diabetic metabolic indicators (such as glycemic variability and HbA1c levels) and cognitive function changes, thereby determining the value of this intelligent screening system in early detection and intervention of cognitive impairment in T2DM patients, which constitutes the key focus of this research.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 40 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Inclusion Criteria
- •Diagnosed with type 2 diabetes mellitus (T2DM).
- •Aged 40-75 years, with self-reported or informant-reported memory complaints.
- •Cognitive assessment scores consistent with either:
- •Cognitive Normal (CN) or
- •Mild Cognitive Impairment (MCI).
- •Preserved ability to perform basic activities of daily living (ADLs).
- •Memory-specific deficits only (e.g., partial forgetfulness), with intact other cognitive domains (e.g., reasoning, judgment, attention, and executive function).
排除标准
- •Other types of diabetes (e.g., type 1 diabetes, gestational diabetes).
- •Education level <6 years.
- •Metabolic disorders that may affect cognition, including:
- •Acute carbohydrate metabolism events within 3 months (e.g., severe hypoglycemia, diabetic ketoacidosis, hyperglycemic hyperosmolar state).
- •Hypothyroidism or other endocrine disorders.
- •Neurological/psychiatric conditions that may impair cognition:
- •History of head trauma, depression, anxiety, delirium, severe pulmonary/renal disease, heart failure, or malignancy.
- •Substance abuse (past 2 years), including nicotine/alcohol dependence.
- •Recent medication use (within 1 month):
- •Anti-Parkinsonian drugs, antiepileptics, sedatives/hypnotics.
- •Cognitive-enhancing drugs (e.g., donepezil, memantine).
- •Inability to complete AI screening or follow-up due to:
- •Speech/hearing/visual impairments or other disabilities
结局指标
主要结局
Early Identification Accuracy of the AI System
时间窗: From enrollment to the end of follow-up for 3-5 years
Sensitivity, specificity, and area under the ROC curve (AUC) of the AI system in detecting Mild Cognitive Impairment (MCI), compared with traditional scales (MMSE/MoCA).
Cognitive Deterioration Rate
时间窗: From enrollment to the end of follow-up for 3-5 years
Conversion rates from:Cognitively Normal (CN) → MCI; MCI → Alzheimer's Disease (AD)
次要结局
- Association Between Diabetic Metabolic Indicators and Cognitive Decline(From enrollment to the end of follow-up for 3-5 years)
- Predictive Value of Multimodal Data(From enrollment to the end of follow-up for 3-5 years)
研究者
Yangyan
Principal Investigator
Tongji Hospital