Hyperglycemia Reduces Efficiency of Brain Networks in Subjects with Type 2 Diabetes

Abstract

Previous research has shown that the brain is an important target of diabetic complications. Since brain regions are interconnected to form a large-scale neural network, we investigated whether severe hyperglycemia affects the topology of the brain network in people with type 2 diabetes. Twenty middle-aged (average age: 54 years) individuals with poorly controlled diabetes (HbA1c: 8.9−14.6%, 74−136 mmol/mol) and 20 age-, sex-, and education-matched healthy volunteers were recruited. Graph theoretic network analysis was performed with axonal fiber tractography and tract-based spatial statistics (TBSS) using diffusion tensor imaging. Associations between the blood glucose level and white matter network characteristics were investigated. Individuals with diabetes had lower white matter network efficiency (P<0.001) and longer white matter path length (P<0.05) compared to healthy individuals. Higher HbA1c was associated with lower network efficiency (r = −0.53, P = 0.001) and longer network path length (r = 0.40, P<0.05). A disruption in local microstructural integrity was found in the multiple white matter regions and associated with higher HbA1c and fasting plasma glucose levels (corrected P<0.05). Poorer glycemic control is associated with lower efficiency and longer connection paths of the global brain network in individuals with diabetes. Chronic hyperglycemia in people with diabetes may disrupt the brain’s topological integration, and lead to mental slowing and cognitive impairment.

Altered brain structural topological properties in type 2 diabetes mellitus patients without complications

“…A short average path length in a WM network facilitates rapid information transfer between different brain regions, which is the basis of cognitive processes . The increased λ in T2DM patients in the present study may reflect disrupted network integration, which is consistent with previous studies on T2DM patients . In T2DM patients, λ was negatively correlated with the ACC of the spatial working memory task, which may reflect that patients with increased λ are worse at dealing with spatial working memory tasks.…”

“…In T2DM patients, λ was negatively correlated with the ACC of the spatial working memory task, which may reflect that patients with increased λ are worse at dealing with spatial working memory tasks. The changing patterns of clustering coefficients reported in topological network studies on T2DM are not consistent . For example, some studies reported a decreased clustering coefficient in T2DM patients, whereas others reported an increase or no change .…”

“…37 The increased λ in T2DM patients in the present study may reflect disrupted network integration, which is consistent with previous studies on T2DM patients. [14][15][16]38 In T2DM patients, λ was negatively correlated with the ACC of the spatial working memory task, which may reflect that patients with increased λ are worse at dealing with spatial working memory tasks. The changing patterns of clustering coefficients reported in topological network studies on T2DM are not consistent.…”

“…Recently, T2DM‐related structural disconnection has been further supported by WM topological network analyses using graph theory. Three studies on structural network analysis in T2DM patients have detected a weaker small‐world property and decreased local or global efficiency in T2DM patients . However, these studies focused either on elderly patients or on patients with diabetes complications and severe cognitive impairment .…”

“…Three studies on structural network analysis in T2DM patients have detected a weaker small‐world property and decreased local or global efficiency in T2DM patients . However, these studies focused either on elderly patients or on patients with diabetes complications and severe cognitive impairment . However, chronic hyperglycemia could lead to end‐organ damage, whereas cognitive impairments have been found to occur in the early stages of T2DM .…”