Introduction: Clinically, detection of disease-causing pathology associated with Alzheimer's disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) is limited to magnetic resonance imaging and positron emission tomography scans, which are expensive and not widely accessible. Here, we assess angiogenic, inflammatory, and AD-related plasma biomarkers to determine their relationships with human post mortem neuropathology.Method: Plasma samples were analyzed using a digital immunoassay and pathological evaluation was performed by University of Kentucky Alzheimer's Disease Research Center neuropathologists. The association of plasma markers with neuropathology was estimated via proportional odds and logistic regressions adjusted for age.Results: Included cases (N = 90) showed increased tau/amyloid beta (Aβ)42 ratio, glial fibrillary acidic protein (GFAP), vascular endothelial growth factor A (VEGF-A), and placental growth factor (PlGF) were positively associated with higher level of AD neuropathological change, while higher Aβ42/Aβ40 ratio was inversely associated. Higher PlGF, VEGF-A, and interleukin 6 were inversely associated with chronic cerebrovascular disease, while Aβ42/Aβ40 ratio was positively associated. Discussion: Our results provide support for the continued study of plasma biomarkers as a clinical screening tool for AD and VCID pathology.

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Alzheimer disease (AD) is a neurodegenerative disease characterized by a cognitive decline leading to dementia. The most impactful genetic risk factor is apolipoprotein E (APOE). APOE-ε4 significantly increases AD risk, APOE-ε3 is the most common gene variant, and APOE-ε2 protects against AD. However, the underlying mechanisms of APOE-ε4 on AD risk remains unclear, with APOE-ε4 impacting many pathways. We investigated how the APOE isoforms associated with the neuroinflammatory state of the brain with and without AD pathology. Frozen brain tissue from the superior and middle temporal gyrus was analyzed from APOE-ε3/3 (n = 9) or APOE-ε4/4 (n = 10) participants with AD pathology and APOE-ε3/3 (n = 9) participants without AD pathology. We determined transcript levels of 757 inflammatory related genes using the NanoString Human Neuroinflammation Panel. We found significant pathways impaired in APOE-ε4/4-AD individuals compared to APOE-ε3/3-AD. Of interest, expression of genes related to microglial activation (SALL1), motility (FSCN1), epigenetics (DNMT1), and others showed altered expression. Additionally, we performed immunohistochemistry of P2RY12 to confirm reduced microglial activation. Our results suggest APOE-ε3 responds to AD pathology while potentially having a harmful long-term inflammatory response, while APOE-ε4 shows a weakened response to pathology. Overall, APOE isoforms appear to modulate the brain immune response to AD-type pathology.

Agglomerative hierarchical clustering analysis (HCA) is a commonly used unsupervised machine learning approach for identifying informative natural clusters of observations. HCA is performed by calculating a pairwise dissimilarity matrix and then clustering similar observations until all observations are grouped within a cluster. Verifying the empirical clusters produced by HCA is complex and not well studied in biomedical applications. Here, we demonstrate the comparability of a novel HCA technique with one that was used in previous biomedical applications while applying both techniques to plasma angiogenic (FGF, FLT, PIGF, Tie-2, VEGF, VEGF-D) and inflammatory (MMP1, MMP3, MMP9, IL8, TNFα) protein data to identify informative subsets of individuals. Study subjects were diagnosed with mild cognitive impairment due to cerebrovascular disease (MCI-CVD). Through comparison of the two HCA techniques, we were able to identify subsets of individuals, based on differences in VEGF (p < 0.001), MMP1 (p < 0.001), and IL8 (p < 0.001) levels. These profiles provide novel insights into angiogenic and inflammatory pathologies that may contribute to VCID.

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