Capillary dysfunction: its detection and causative role in dementias and stroke

Abstract

In acute ischemic stroke, critical hypoperfusion is a frequent cause of hypoxic tissue injury: As cerebral blood flow (CBF) falls below the ischemic threshold of 20 mL/100 mL/min, neurological symptoms develop and hypoxic tissue injury evolves within minutes or hours unless the oxygen supply is restored. But is ischemia the only hemodynamic source of hypoxic tissue injury? Reanalyses of the equations we traditionally use to describe the relation between CBF and tissue oxygenation suggest that capillary flow patterns are crucial for the efficient extraction of oxygen: without close capillary flow control, "functional shunts" tend to form and some of the blood's oxygen content in effect becomes inaccessible to tissue. This phenomenon raises several questions: Are there in fact two hemodynamic causes of tissue hypoxia: Limited blood supply (ischemia) and limited oxygen extraction due to capillary dysfunction? If so, how do we distinguish the two, experimentally and in patients? Do flow-metabolism coupling mechanisms adjust CBF to optimize tissue oxygenation when capillary dysfunction impairs oxygen extraction downstream? Cardiovascular risk factors such as age, hypertension, diabetes, hypercholesterolemia, and smoking increase the risk of both stroke and dementia. The capillary dysfunction phenomenon therefore forces us to consider whether changes in capillary morphology or blood rheology may play a role in the etiology of some stroke subtypes and in Alzheimer's disease. Here, we discuss whether certain disease characteristics suggest capillary dysfunction rather than primary flow-limiting vascular pathology and how capillary dysfunction may be imaged and managed.

Fig. 1

Panel a illustrates how MTT and CTH can be determined by two-photon microscopy, tracking the passage of a fluorescein isothiocyanate (FITC) bolus as it passes through the microvasculature of a mouse brain. The top panels show images obtained through a cranial window 8 and 20 s after injection. Arteries and veins can be identified based on the arrival of the fluorescent dye and their concentration-time curves (CTC) measured over time (lower left panel). The parameters of the transit time distribution (lower right) are then fitted so that the venous outflow curve is accurately predicted (lower left panel). The delay between arterioles and venules defines MTT, and CTH is defined as the standard deviation of the transit time distribution (lower right panel). Previously unpublished data. Panel b shows the probability that CoV correlated with mini-mental state examination (MMSE) scores in 16 patients with clinically suspected possible or probable Alzheimer’s disease (AD). Note the significant, negative correlations between MMSE and cortical CoV. AD was verified by ICD-10, DSM-IV, and NINCDS-ADRDA. The age of the patients was 70.4 ± 6.3 years and their MMSE 24.8 ± 2.7. Note the strong temporoparietal, cingulate, and precuneus involvement. In AD, these regions typically reveal abnormally low fluorodeoxyglucose uptake and cortical thinning. Data were acquired after informed consent in a project approved by the regional Ethics Committee. Previously unpublished data. Panel c shows maps of acute MTT, CTH, CoV, OEF^max, and apparent diffusion coefficient (ADC) in a 74-year-old male with a distal occlusion of the middle cerebral artery, imaged 4 h and 14 min after symptom onset. His NIHSS score was 14. Twenty days later, follow-up (FoUp) FLAIR images were acquired to assess the extent of tissue damage. The extent of hypoperfusion is visualized as areas of prolonged MTT (green, yellow, and orange colors indicate higher values), while disturbed capillary flow patterns can be identified as elevated CTH values. Note that white matter hyperintensities, indicative of cerebral small-vessel disease (SVD) are present in this patient. These can be recognized as confluent hyperintensities in the ADC image and bright lesions in FLAIR images. Reproduced from [••] with permission from the publisher. Panel d shows PET and MR data from two patients with occlusion of their right carotid artery. Both had experienced short episodes of left-sided hemiparesis and right-sided amaurosis fugax (blindness), but experienced no neurological deficits at the time of the study. Occlusion of their carotid arteries was diagnosed by ultrasonic examination, and competing cerebral pathologies were excluded by earlier MRI. The PET protocol, experimental procedures, and PET data have previously been published in [86]. The PWI data presented here were obtained after informed consent as part of the original study protocol which was approved by the local ethics committee. The PET images were manually registered (MNI Register, McConnell Brain Imaging Centre of the Montreal Neurological Institute, McGill University) to patient MRIs using an affine transformation. Then, PET OEF maps were generated for 11 slices, corresponding to the location of 11 midbrain PWI slices in which prolonged MTT could easily be observed. The three panels on the left compares MTT and OEF^max images, obtained by MRI, to OEF maps obtained by PET, at two slice locations in one of the patients. In the two plots to the right, the ability of MTT and OEF^max to predict “true” OEF is compared for the hemispheres ipsi- and contralateral to the stenosis (red and green dots) in 11 slices each of the two patients. The higher OEF in the affected side has traditionally been associated with severe hypoperfusion caused by carotid stenosis. See text. Previously unpublished data