Causal Structural Covariance Network Suggesting Structural Alterations Progression in Type 2 Diabetes Patients

Abstract

Background and PurposeAccording to reports, type 2 diabetes (T2D) is a progressive disease. However, no known research has examined the progressive brain structural changes associated with T2D. The purpose of this study was to determine whether T2D patients exhibit progressive brain structural alterations and, if so, how the alterations progress.Materials and MethodsStructural magnetic resonance imaging scans were collected for 81 T2D patients and 48 sex-and age-matched healthy controls (HCs). Voxel-based morphometry (VBM) and causal structural covariance network (CaSCN) analyses were applied to investigate gray matter volume (GMV) alterations and the likely chronological processes underlying them in T2D. Two sample t-tests were performed to compare group differences, and the differences were corrected using Gaussian random field (GRF) correction (voxel-level p < 0.001, cluster-level p < 0.01).ResultsOur findings demonstrated that GMV alterations progressed in T2D patients as disease duration increased. In the early stages of the disease, the right temporal pole of T2D patients had GMV atrophy. As the diseases duration prolonged, the limbic system, cerebellum, subcortical structures, parietal cortex, frontal cortex, and occipital cortex progressively exhibited GMV alterations. The patients also exhibited a GMV alterations sequence exerting from the right temporal pole to the limbic-cerebellum-striatal-cortical network areas.ConclusionOur results indicate that the progressive GMV alterations of T2D patients manifested a limbic-cerebellum-striatal-cortical sequence. These findings may contribute to a better understanding of the progression and an improvement of current diagnosis and intervention strategies for T2D.

A systematic review of the impact of type 2 diabetes on brain cortical thickness

“…The main goal of each reviewed study was to assess the regional effects of T2D, whether in the presence or absence of other conditions, on cerebral cortical thickness as measured on T1-weighted MRI. Some studies dug deeper by, for example, evaluating the association between cognitive function and brain morphology in T2D patients, including the effect of various confounders such as age, years of education, and disease duration (Peng et al, 2014 ; Reynolds et al, 2023 ); investigating the relationship between fasting and non-fasting physiology with gray matter and white matter volume (Markus et al, 2017 ; Honea et al, 2022 ); measuring the association between neuropsychological deficits with structural and functional brain alternations (Garcia-Casares et al, 2014 ; Zhang et al, 2022 , 2023 ); or comparing the effect of metabolic and vascular factors on cortical and subcortical gray matter structural alterations (Korf et al, 2006 ; Brundel et al, 2010 ; Tchistiakova et al, 2014 ; Bernardes et al, 2018 ; Cui et al, 2023 ; Moreno et al, 2023 ; Reynolds et al, 2023 ). Other studies compared the impact of T2D on brain morphology not only against controls, but also prediabetes (Jing et al, 2023 ; Monereo-Sánchez et al, 2023 ), major depression (Ajilore et al, 2010 ) or mild cognitive impairment and Alzheimer's disease (Moran et al, 2019 ; Palix et al, 2022 ).…”

“…We have already noted that Freesurfer was used in the majority of studies, a software that has been shown to have high accuracy and reliability in measuring cortical thickness (Gronenschild et al, 2012 ). Other toolkits included SPM8 (Garcia-Casares et al, 2014 ; Gao et al, 2023 ) and SPM12 (Roy et al, 2020 ; Palix et al, 2022 ; Zhang et al, 2022 ), MRIcroN (Roy et al, 2020 ), REST (Roy et al, 2020 ), VSRAD (Hayashi et al, 2011 ), MarsBaR (Ferreira et al, 2017 ), MEDx (Korf et al, 2006 ), PM12 (Crisóstomo et al, 2021 ) and CAT12 (Crisóstomo et al, 2021 ), Computational Anatomy Toolbox 12 (CAT12: http://www.neuro.uni-jena.de/cat/ ) (Chen et al, 2022 ; Zhang et al, 2023 ). We observed that almost all discrepant studies (6 out of 10) predominantly utilized newer versions of Freesurfer, specifically versions 5.3 and 6.0.…”

Spatiotemporal consistency analysis of cerebral small vessel disease: an rs-fMRI study

“…Previous studies have shown that patients with SVD have reduced functional connectivity in the frontal and temporal lobes ( Sun et al, 2011 ; Gu et al, 2022 ; Yin et al, 2022 ). Vascular risk factors such as hypertension and diabetes affect the gray matter volume in the frontal and temporal lobes ( Gold et al, 2005 ; Li et al, 2021 ; Zhang et al, 2022 ). In this study, SVD patients with hypertension or diabetes were not excluded.…”