Li Yi-Fan, Wei Yue, Li Ming-Rui, Sun Zhi-Zhong, Xie Wei-Yan, Li Qian-Fan, Xie Chen-Hui, Xiang Jing-Yi, Tan Xin, Qiu Shi-Jun, Liang Yi
Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong Province, China.
Department of Basic Psychology, School of Psychology, HKUST-Shenzhen Research Institute, Shenzhen 518060, Guangdong Province, China.
World J Diabetes. 2025 Jul 15;16(7):103468. doi: 10.4239/wjd.v16.i7.103468.
Cognitive decline in type 2 diabetes mellitus (T2DM) occurs years before the onset of clinical symptoms. Early detection of this incipient cognitive decline stage, which is T2DM without mild cognitive impairment, is critical for clinical intervention, yet it remains elusive and challenging to identify.
To identify structural changes in the brains of T2DM patients without cognitive impairment to gain insights into the early-stage cognitive decline.
Using diffusion tensor imaging (DTI), we constructed structural brain networks in 47 T2DM patients and 47 age-/sex-matched healthy controls. Machine learning models incorporating connectivity features were developed to classify T2DM brains and predict disease duration.
T2DM patients exhibited reduced global/local efficiency and small-worldness, alongside weakened connectivity in cortical regions but enhanced subcortical-frontal connections, suggesting compensatory mechanisms. A classification model leveraging 18 connectivity features achieved 92.5% accuracy in distinguishing T2DM brains. Structural connectivity patterns further predicted disease onset with an error of ± 1.9 years.
Our findings reveal early-stage brain network reorganization in T2DM, highlighting subcortical-frontal connectivity as a compensatory biomarker. The high-accuracy models demonstrate the potential of DTI-based biomarkers for preclinical cognitive decline detection.
2型糖尿病（T2DM）患者的认知功能下降在临床症状出现前数年就已发生。早期发现这一处于T2DM但无轻度认知障碍的初始认知功能下降阶段，对于临床干预至关重要，但目前仍难以识别且具有挑战性。
识别无认知障碍的T2DM患者大脑的结构变化，以深入了解早期认知功能下降情况。
利用扩散张量成像（DTI），我们构建了47例T2DM患者和47例年龄及性别匹配的健康对照者的脑结构网络。开发了纳入连接特征的机器学习模型，以对T2DM患者的大脑进行分类并预测病程。
T2DM患者表现出整体/局部效率降低和小世界特性减弱，同时皮质区域连接减弱，但皮质下-额叶连接增强，提示存在代偿机制。利用18个连接特征的分类模型在区分T2DM患者大脑方面的准确率达到92.5%。结构连接模式进一步预测疾病发病时间，误差为±1.9年。
我们的研究结果揭示了T2DM患者早期脑网络重组，突出了皮质下-额叶连接作为一种代偿生物标志物。高精度模型证明了基于DTI的生物标志物在临床前认知功能下降检测中的潜力。
