The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
Sci Rep. 2023 Mar 9;13(1):3940. doi: 10.1038/s41598-023-28163-5.
Type 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer's disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear. To answer this question, we analyzed the rs-fMRI data of 37 patients with T2DM and mild cognitive impairment (T2DM-MCI), 93 patients with T2DM but no cognitive impairment (T2DM-NCI), and 69 normal controls (NC). We achieved an accuracy of 87.91% in T2DM-MCI versus T2DM-NCI classification and 80% in T2DM-NCI versus NC classification using the XGBoost model. The thalamus, angular, caudate nucleus, and paracentral lobule contributed most to the classification outcome. Our findings provide valuable knowledge to classify and predict T2DM-related CI, can help with early clinical diagnosis of T2DM-MCI, and provide a basis for future studies.
2 型糖尿病（T2DM）与认知能力下降和大脑结构及功能改变密切相关。静息态功能磁共振成像（rs-fMRI）用于诊断神经退行性疾病，如认知障碍（CI）、阿尔茨海默病（AD）和血管性痴呆（VaD）。然而，T2DM 伴轻度认知障碍（T2DM-MCI）患者的功能连接（FC）是否有助于早期诊断尚不清楚。为了回答这个问题，我们分析了 37 例 T2DM 伴轻度认知障碍（T2DM-MCI）患者、93 例 T2DM 但无认知障碍（T2DM-NCI）患者和 69 名正常对照（NC）的 rs-fMRI 数据。我们使用 XGBoost 模型在 T2DM-MCI 与 T2DM-NCI 分类中达到了 87.91%的准确性，在 T2DM-NCI 与 NC 分类中达到了 80%的准确性。丘脑、角回、尾状核和中央旁小叶对分类结果的贡献最大。我们的研究结果为分类和预测 T2DM 相关 CI 提供了有价值的知识，有助于 T2DM-MCI 的早期临床诊断，并为未来的研究提供了基础。
