Diffusion Tensor Imaging Of the Brain in Type 1 Diabetes

Abstract

Individuals with Type 1 diabetes mellitus (T1DM) are required to carefully manage their insulin dosing, dietary intake, and activity levels in order to maintain optimal blood sugar levels. Over time, exposure to hyperglycaemia is known to cause significant damage to the peripheral nervous system, but its impact on the central nervous system has been less well studied. Researchers have begun to explore the cumulative impact of commonly experienced blood glucose fluctuations on brain structure and function in patient populations. To date, these studies have typically used magnetic resonance imaging to measure regional grey and white matter volumes across the brain. However, newer methods, such as diffusion tensor imaging (DTI) can measure the microstructural properties of white matter, which can be more sensitive to neurological effects than standard volumetric measures. Studies are beginning to use DTI to understand the impact of T1DM on white matter structure in the human brain. This work, its implications, future directions, and important caveats, are the focus of this review.

Exploratory Multisite MR Spectroscopic Imaging Shows White Matter Neuroaxonal Loss Associated with Complications of Type 1 Diabetes in Children

“…6,7 T1D and hyperglycemia have also been negatively associated with localized DTI microstructural measures of brain health. 8 Prior studies have also identified similar regional changes in brain function. Differences in activation across the cingulum, hypothalamus, cortex, deep GM nuclei, and other regions have been observed with regard to changes in working memory and visual and motor processes as well as T1D severity.…”

“…This finding builds on prior DTI studies in this population that have suggested a relationship between hyperglycemia and WM biomarkers and represents a novel exploration of diffuse WM neurochemistry, as opposed to focal investigations performed in prior single-voxel MRS studies. 8,[13][14][15][16] While small sample sizes can produce underpowered or overfit models, we are reassured that this effect was significant throughout the forward model selection process and was not suppressed by additional covariates. Furthermore, we found that the best model identified through model selection explained roughly 40% of the variance in the data, indicating high explainability in a noisy system with a small sample size.…”