Morphological and pathological evolution of the brain microcirculation in aging and Alzheimer's disease

Abstract

Key pathological hallmarks of Alzheimer's disease (AD), including amyloid plaques, cerebral amyloid angiopathy (CAA) and neurofibrillary tangles do not completely account for cognitive impairment, therefore other factors such as cardiovascular and cerebrovascular pathologies, may contribute to AD. In order to elucidate the microvascular changes that contribute to aging and disease, direct neuropathological staining and immunohistochemistry, were used to quantify the structural integrity of the microvasculature and its innervation in three oldest-old cohorts: 1) nonagenarians with AD and a high amyloid plaque load; 2) nonagenarians with no dementia and a high amyloid plaque load; 3) nonagenarians without dementia or amyloid plaques. In addition, a non-demented (ND) group (average age 71 years) with no amyloid plaques was included for comparison. While gray matter thickness and overall brain mass were reduced in AD compared to ND control groups, overall capillary density was not different. However, degenerated string capillaries were elevated in AD, potentially suggesting greater microvascular "dysfunction" compared to ND groups. Intriguingly, apolipoprotein ε4 carriers had significantly higher string vessel counts relative to non-ε4 carriers. Taken together, these data suggest a concomitant loss of functional capillaries and brain volume in AD subjects. We also demonstrated a trend of decreasing vesicular acetylcholine transporter staining, a marker of cortical cholinergic afferents that contribute to arteriolar vasoregulation, in AD compared to ND control groups, suggesting impaired control of vasodilation in AD subjects. In addition, tyrosine hydroxylase, a marker of noradrenergic vascular innervation, was reduced which may also contribute to a loss of control of vasoconstriction. The data highlight the importance of the brain microcirculation in the pathogenesis and evolution of AD.

Figure 1. Capillary density in gray matter and white matter.

Sections from all cases were stained with collagen IV. A) Gray matter image from an AD case. B) Gray matter image from a YO-NPC case. C) White matter image from an ND-HPC case. D) White matter image from an OO-NPC case. E-F) Capillary density was determined by image analysis using ImageJ software as described in the Materials and Methods section. Each data point represents the average % area covered by capillaries in numerous images from a single case. Bars represent the average of all cases and error bars represent the SEM for all cases in a group. E) Gray matter capillary density. F) White matter capillary density. Groups were not significantly different as determined by One-way ANOVA. The scale bar is applicable to all images in Figure 1.

Figure 2. Brain mass and gray matter thickness.

A) Total brain mass was determined at the time of autopsy for each case. B) Gray matter thickness for each case was determined by numerous straight-line measurements of the gray matter ribbon on sections from each case. Represented in each data point is the average of the lowest 30% of measurements for each case. In graphs A and B, bars represent the average for all cases in the group and error bars represent the SEM. Statistical analysis: One-way ANOVA was performed for brain mass (p = 0.047) and for gray matter thickness (p = 0.0012). A Tukey post test was performed for pair-wise comparison of all groups and significance is indicated on the graphs.

Figure 3. Blood vessel abnormalities in the oldest-old.

A) An area of high string vessel density in an AD case. String vessels are indicated by arrows. B) A string vessel exhibiting a microembolus (arrow) in an AD case. C) Abnormally bundled capillaries in an AD case. D) A large tortuous vessel in the white matter of an AD case. All sections were stained for collagen IV.

Figure 4. Distribution of string vessels in the four studied groups.

A) The number of string vessels was determined by counting string vessels in collagen IV-stained sections. Each data point represents the average number of string vessels per field for numerous images for each case. Bars represent the average of all cases in a group and error bars represent the SEM. Statistical analysis: One-way ANOVA revealed statistically increased string vessel numbers in AD compared to ND control groups; p = 0.0076, with a Tukey post test indicating statistical increase in AD compared to OO-NPC and YO-NPC groups. B) String vessel number stratified according to ApoE ε4 carrier status. Statistical analysis: t-test reveals a significant increase in string vessels in ApoE ε4 carriers (p = 0.0044). C) String vessel number of ApoE ε3/3 individuals with or without amyloid plaques. Statistical analysis: t-test reveals a significant increase in string vessel number in cases with amyloid plaques (p = 0.0027).

Figure 5. Vascular innervation of the oldest-old.

A-B) The amount of innervation of the cortex by cholinergic neurons was determined by staining with VAChT. A) An example of low VAChT staining in an AD case. B) An example of high VAChT staining in a YO-NPC case. C-D) The amount of innervation of the cortex by noradrenergic neurons was determined by staining with TH. C) An example of low TH staining in AD case. D) An example of high TH staining in a YO-NPC case gray matter. E) White matter TH staining in an OO-NPC. F) Cholinergic innervation of cortical gray matter was quantified by counting the number of VAChT-positive vesicles per field using ImageJ software as described in the Materials and Methods section. Each point represents the average number of vesicles observed in each case. G) Noradrenergic innervation of cortical gray matter, as determined by TH staining, was quantified by determining the % area stained in each image. Each point represents the average % area observed in each case. H) The TH immunoreactivity in cortical white matter was quantified as in the gray matter. For all graphs, bars represent the average of all cases for each group with their SEM. Groups were not significantly different by One-way ANOVA. Magnification: A and B = 200X; C, D and E = 100X.

Figure 6. Brain fitness index.

The overall brain fitness of each individual was determined by statistical analysis of 13 parameters described in Materials and Methods section and shown in Table 1 and Table S1. The BFI represents the mathematical average of all the z-scores for each individual. Bars represent the average of all cases for each group with error bars representing the SEM. Statistical analysis: One-way ANOVA demonstrated statistical difference (F = 7.21 df = (2, 22), p = 0.004). Bonferroni adjustment found that the AD and ND-HPC groups were not significantly different (p = 0.06) and that the AD and NPC groups were significantly different (p = 0.003). The ND-HPC and NPC groups were not significantly different (p = 0.73). Both AD and ND-HPC had a negative BFI relative to the NPC group, being more negative in the AD group.

Figure 7. A representation of microvascular compromise in AD.

On the left is a representation of the cerebral cortex of an individual with an optimal vasculature and efficient cerebral blood flow with no amyloid plaques, sparse string vessels and no gray matter atrophy. On the right is a depiction of pathological condition in which the capillary density is relatively maintained, but with decreased cerebral blood flow resulting from numerous string vessels. Whether string vessels are the cause or effect of grey matter hypoperfusion and eventual brain atrophy is unknown. However, evidence suggests that parenchymal plaques may initially be associated with capillaries and arterioles in an attempt to seal microvascular leakage . At more advanced stages, the pressure exerted by the growing perivascular amyloid deposits constricts the microvessel, leaving dysfunctional capillary stumps. In addition, amyloid deposits associated with larger cortical and leptomeningeal vessels ultimately destroy vascular smooth muscle and endothelial cells. Brain perfusion is further damaged by a compromised interstitial fluid drainage due to destruction of the perivascular spaces .