Cortical gray matter microstructural alterations in patients with type 2 diabetes mellitus. Issue 10 (4th September 2022)

• Record Type: Journal Article
• Authors: Huang, Haoming; Ma, Xiaomeng; Yue, Xiaomei; Kang, Shangyu; Rao, Yawen; Long, Wenjie; Liang, Yi; Li, Yifan; Chen, Yuna; Lyu, Wenjiao; Wu, Jinjian; Tan, Xin; Qiu, Shijun
• Is Part Of: Brain and behavior. Volume 12:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-04
• Subjects: diffusion tensor imaging -- gray matter microstructural alterations -- gray matter‐based spatial statistics -- neurite orientation dispersion and density imaging -- support vector machine -- type 2 diabetes mellitus; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.2746
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Background and purpose: 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. Methods: 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. Results: 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%. Conclusions: 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. Abstract : Detecting early cortical microstructural alterations in type 2 diabetes mellitus (T2DM) patients without cognition decline with gray matter‐based spatial statistics. Significantly lower intracellular volume fractions (ICVF) values were observed in nearly whole brain regions of T2DM patients. A linear support vector machine model was trained with cortical ICVF 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%.