The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.
Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.
Brain Behav. 2022 Oct;12(10):e2746. doi: 10.1002/brb3.2746. Epub 2022 Sep 4.
Neurodegenerative processes are widespread in the brains of type 2 diabetes mellitus (T2DM) patients; gaps remain to exist in the current knowledge of the associated gray matter (GM) microstructural alterations.
A cross-sectional study was conducted to investigate alterations in GM microarchitecture in T2DM patients by diffusion tensor imaging and neurite orientation dispersion and density imaging (NODDI). Seventy-eight T2DM patients and seventy-four age-, sex-, and education level-matched healthy controls (HCs) without cognitive impairment were recruited. Cortical macrostructure and GM microstructure were assessed by surface-based analysis and GM-based spatial statistics (GBSS), respectively. Machine learning models were trained to evaluate the diagnostic values of cortical intracellular volume fraction (ICVF) for the classification of T2DM versus HCs.
There were no differences in cortical thickness or area between the groups. GBSS analysis revealed similar GM microstructural patterns of significantly decreased fractional anisotropy, increased mean diffusivity and radial diffusivity in T2DM patients involving the frontal and parietal lobes, and significantly lower ICVF values were observed in nearly all brain regions of T2DM patients. A support vector machine model with a linear kernel was trained to realize the T2DM versus HC classification and exhibited the highest performance among the trained models, achieving an accuracy of 74% and an area under the curve of 83%.
NODDI may help to probe the widespread GM neuritic density loss in T2DM patients occurs before measurable macrostructural alterations. The cortical ICVF values may provide valuable diagnostic information regarding the early GM microstructural alterations in T2DM.
2 型糖尿病(T2DM)患者的大脑中存在广泛的神经退行性过程;目前对于相关灰质(GM)微观结构改变的认识仍存在空白。
进行了一项横断面研究,通过弥散张量成像和神经丝取向分散和密度成像(NODDI)来研究 T2DM 患者的 GM 微观结构改变。招募了 78 名 T2DM 患者和 74 名年龄、性别和教育程度匹配的无认知障碍的健康对照者(HCs)。通过基于表面的分析和基于 GM 的空间统计学(GBSS)分别评估皮质宏观结构和 GM 微观结构。训练机器学习模型以评估皮质细胞内容积分数(ICVF)对 T2DM 与 HCs 分类的诊断价值。
两组之间的皮质厚度或面积没有差异。GBSS 分析显示,T2DM 患者的 GM 微观结构模式相似,表现为额顶叶区域的各向异性分数降低、平均弥散度和径向弥散度增加,并且 T2DM 患者的几乎所有脑区的 ICVF 值均较低。使用线性核的支持向量机模型被训练来实现 T2DM 与 HCs 的分类,在训练模型中表现出最高的性能,准确率为 74%,曲线下面积为 83%。
NODDI 可能有助于探究 T2DM 患者广泛的 GM 神经纤维密度丢失发生在可测量的宏观结构改变之前。皮质 ICVF 值可能为 T2DM 患者的 GM 微观结构早期改变提供有价值的诊断信息。