ArticleBrain and behavior2022
Cortical gray matter microstructural alterations in patients with type 2 diabetes mellitus.
Article in Brain and behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 6 citations in OpenAlex.
- Altered cortical thickness in type 2 diabetes mellitus patients revealed by coordinate-based meta-analysis.BMC endocrine disorders · 2026Pooled it
- A systematic review of the impact of type 2 diabetes on brain cortical thickness.Frontiers in dementia · 2024Pooled it
- Diffusion Magnetic Resonance Imaging Models for Detecting Brain Microstructural Abnormalities in Type 2 Diabetes: A Systematic Review.Bioengineering (Basel, Switzerland) · 2026Review
- White and gray matter diffusion-derived differences in type 2 diabetes mellitus: a free-water-corrected diffusion MRI study.Frontiers in medicine · 2026Article
- Impact of glycemic control on brain microstructure in type 2 diabetes mellitus: insights from diffusion tensor imaging.BMC medical imaging · 2025Article
- Micro- and macro-changes in early-stage type 2 diabetes mellitus without cognitive impairment: a diffusion tensor imaging (DTI) and surface-based morphometry (SBM) study.Frontiers in neurology · 2023Article
- Cortical gray matter microstructural alterations in patients with type 2 diabetes mellitus.Brain and behavior · 2022Article
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeNeurodegenerative 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.
methodsA 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.
resultsThere 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%.
conclusionsNODDI 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.
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