Investigation of type 2 diabetes mellitus and mild cognitive impairment: a study based on diffusion tensor imaging analysis along the perivascular space and peak width of skeletonized mean diffusivity

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Background: Mild cognitive impairment (MCI) is a common complication of type 2 diabetes mellitus (T2DM); however, its underlying pathogenesis remains unclear. This study aimed to employ diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) and peak width of skeletonized mean diffusivity (PSMD) to investigate changes in the perivascular space (PVS) microenvironment in T2DM. Methods: Patients with T2DM (24 with MCI and 23 without MCI) and healthy controls (HCs) (n=26) were prospectively recruited. All participants underwent the Montreal cognitive assessment (MoCA) and diffusion tensor imaging (DTI). The analysis along the perivascular space (ALPS) index was calculated using the FMRIB software library (FSL), and PSMD was derived based on tract-based spatial statistics (TBSS). One-way analysis of variance (ANOVA) was used for comparisons among three groups. Correlations of the ALPS index and PSMD with MoCA scores, fasting blood glucose (FBG) levels, and disease duration were analyzed. Results: The ALPS index was significantly lower in the type 2 diabetes mellitus with mild cognitive impairment (T2DM-MCI) (mean ± standard deviation: 1.324±0.170) and type 2 diabetes mellitus without mild cognitive impairment (T2DM-nMCI) (1.362±0.142) groups than in the HC group (1.465±0.104) (P<0.05), while PSMD (×10-4 mm2/s) was significantly higher in the T2DM-MCI group [median (25th and 75th percentile): 2.139 (1.895-2.306)] than in the T2DM-nMCI [1.868 (1.777-2.026)] and HC [1.888 (1.776-2.111)] groups (P<0.05). PSMD was negatively correlated with MoCA scores (r=-0.425, P=0.003) and positively correlated with disease duration (r=0.433, P=0.002). The ALPS index was negatively correlated with FBG levels, disease duration, and PSMD (r=-0.382, P=0.008; r=-0.420, P=0.003; r=-0.333, P=0.022, respectively). Conclusions: The combined use of DTI-ALPS and PSMD provides a robust, non-invasive dual-biomarker framework that may advance understanding of the mechanisms underlying diabetic MCI and serve as a potential imaging biomarker.

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