| Literature DB >> 27239779 |
Toshitaka Umemura1, Takahiko Kawamura2,3, Nigishi Hotta2.
Abstract
Diabetes patients have more than double the risk of ischemic stroke compared with non-diabetic individuals, and its neuroimaging characteristics have important clinical implications. To understand the pathophysiology of ischemic stroke in diabetes, it is important to focus not only on the stroke subtype, but also on the size and location of the occlusive vessels. Specifically, ischemic stroke in diabetes patients might be attributed to both large and small vessels, and intracranial internal carotid artery disease and small infarcts of the posterior circulation often occur. An additional feature is that asymptomatic lacunar infarctions are often seen in the basal ganglia and brain stem on brain magnetic resonance imaging. In particular, cerebral small vessel disease (SVD), including lacunar infarctions, white matter lesions and cerebral microbleeds, has been shown to be associated not only with stroke incidence, but also with the development and progression of dementia and diabetic microangiopathy. However, the pathogenesis of cerebral SVD is not fully understood. In addition, data on the association between neuroimaging findings of the cerebral SVD and diabetes are limited. Recently, the clinical importance of the link between cerebral SVD and retinal microvascular abnormalities has been a topic of considerable interest. Several clinical studies have shown that retinal microvascular abnormalities are closely related to cerebral SVD, suggesting that retinal microvascular abnormalities might be pathophysiologically linked to ischemic cerebral SVD. We review the literature relating to the pathophysiology and neuroimaging of cerebrovascular disease in diabetes, and discuss the problems based on the concept of cerebral large and small vessel disease.Entities:
Keywords: Cerebral large and small vessel disease; Diabetic retinopathy; Type 2 diabetes
Mesh:
Substances:
Year: 2016 PMID: 27239779 PMCID: PMC5334292 DOI: 10.1111/jdi.12545
Source DB: PubMed Journal: J Diabetes Investig ISSN: 2040-1116 Impact factor: 4.232
Figure 1Schematic representation of the possible role of the polyol pathway in diabetes atherosclerosis. The polyol pathway consists of two steps: glucose is first reduced to sorbitol by the enzyme, aldose reductase (AR), and the resulting sorbitol is then changed to fructose by sorbitol dehydrogenase (SDH). During euglycemia, the utilization of glucose through the polyol pathway accounts for less than 3% of glucose consumption in cells. However, during hyperglycemia, total consumption of glucose through this pathway represents up to 30%137, resulting in the enhancement of glucose utilization through metabolic cascade shown. Thus, hyperglycemia‐induced polyol pathway hyperactivity might contribute to developing not only microvascular disease, but also atherosclerosis in the patients with diabetes. eNOS, endothelial nitric oxide synthase; MMP, matrix metalloproteinases; NAMPT, nicotineamide phosphoribosyl transferase; NO, nitric oxide; PKC, protein kinase C; TF, tissue factor; TX, thromboxane; VCAM, vascular cell adhesion molecule.
Figure 2Extracranial carotid artery disease. A 75‐year‐old man with symptomatic carotid artery stenosis. (a,b) Reconstructed computed tomography angiography and 3‐D computed tomography angiography show severe stenosis of the left internal carotid artery (arrows). (c) Unstable plaque is visualized as a hyperintense signal on axial fat‐suppressed black‐blood T1‐weighted image (arrow).
Figure 3Intracranial carotid artery disease. A 66‐year‐old woman with an ipsilateral transient ischemic attack. Magnetic resonance imaging angiography volume rendering image shows severe stenosis in the right intracranial internal carotid artery.
Figure 4Example of infratentorial branch atheromatous lesion extending to the basal surface of the pons on diffusion‐weighted images. (a) Axial image. (b) Coronal image. (c) Magnetic resonance imaging angiography shows mild stenosis of the basilar artery. (d,e) Example of supratentorial branch atheromatous lesion in the left lenticulostriate artery territory on diffusion‐weighted images. (d) Axial image. (e) Coronal image. A 73‐year‐old man presented progressive motor deficits on day 2 after symptom onset. (f) The infarct is located in the posterior segment of the lenticulostriate artery territory, and the corticospinal tracts (red cables) are shown to cross the lenticulostriate artery territory in the posterosuperior segment on diffusion tensor imaging.
Figure 5Magnetic resonance imaging expressions of cerebral small vessel disease. (a) New lacunes (arrows) in the basal ganglia and lateral ventricular anterior horn have appeared on 8‐year follow‐up fluid‐attenuated inversion recovery images. (b) Periventricular white matter lesions (open circle) extend into deep white matter over 6‐year follow up. (c) Gradient‐recalled echo T2*‐weighted magnetic resonance imaging of patients who had developed new microbleeds without cardiovascular events over 3‐year follow up. Arrows indicate new microbleeds on the follow‐up scan.
Cross‐sectional and longitudinal relationship between type 2 diabetes and magnetic resonance imaging measures
| Author, reference (year of publication) | Study design | Mean follow‐up period (years) | Participants ( | Mean age (years) | Mean diabetes duration (years) | Outcome measures results |
| Adjustment variables |
|---|---|---|---|---|---|---|---|---|
|
Manschot | Cross‐sectional | – |
T2DM |
66.1 |
8.8 |
PWML (Scheltens scale 0–12) |
| NA |
|
van Harten | Cross‐sectional | – |
T2DM with HT |
73.5 |
11.9 |
PVH (Scheltens score 0–6) |
NS | NA |
|
Jongen | Cross‐sectional | – |
T2DM |
65.9 |
8.7 |
Total brain volume |
| Age, sex, intracranial volume and education level |
|
Umemura | Cross‐sectional | – |
T2DM |
59.6 |
11.9 |
SBI | NS | NA |
|
De Bresser | Longitudinal | 4 |
T2DM |
65.9 |
9.5 |
Total brain volume |
NS | Age and sex |
|
Van Elderen | Longitudinal | 3 |
DM |
74.7 | NA |
Total brain atrophy (%) |
| Age and sex |
|
Espeland |
Cross‐sectional |
– |
T2DM |
77.8 | NA |
Total brain atrophy –3.02 cc (diff) |
| Age, clinic site, WHI treatment, time from WHI enrollment, time between scans and baseline volume |
|
Moran | Cross‐sectional | – |
T2DM |
67.8 |
7.0 |
Gray matter volume |
|
Age, sex and total intracranial volume |
*Data are mean (95% confidence interval). **Data are β (95% confidence interval). ***Data are mean (standard error). †White matter lesions (WML) volumes are analyzed as log‐transformed values. DM, diabetes mellitus; DWML, deep white matter lesion; NA, not applicable; NS, not significant; PVH, periventricular hyperintensity; PWML, periventricular white matter lesion; SBI; silent brain infarction; T2DM; type 2 diabetes mellitus; WMH, white matter hyperintensity.
Figure 6Cerebro–retinal interaction in diabetes. A 76‐year‐old woman with simple diabetic retinopathy. (a) Magnetic resonance imaging expressions of cerebral small vessel disease including silent brain infarction (red arrow), white matter lesion (white arrow) and microbleed (arrow head). (b) Retinal photograph of diabetic retinopathy signs showing microaneurysm and retinal hemorrhages (arrow), and hard exudates (arrow head).