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. 2021 Dec 31;13(1):e12253.
doi: 10.1002/dad2.12253. eCollection 2021.

Cerebrovascular stiffness and flow dynamics in the presence of amyloid and tau biomarkers

Leonardo A Rivera-Rivera  1   2   3 , Laura Eisenmenger  4 , Karly A Cody  1 , Thomas Reher  4 , Tobey Betthauser  1   2 , Robert V Cadman  1   2 , Howard A Rowley  1   4 , Cynthia M Carlsson  1   5 , Nathaniel A Chin  1   2 , Sterling C Johnson  1   2   5 , Kevin M Johnson  3   4
Affiliations

Cerebrovascular stiffness and flow dynamics in the presence of amyloid and tau biomarkers

Leonardo A Rivera-Rivera et al. Alzheimers Dement (Amst). .

Abstract

Introduction: This work investigated the relationship between cerebrovascular disease (CVD) markers and Alzheimer's disease (AD) biomarkers of amyloid beta deposition, and neurofibrillary tau tangles in subjects spanning the AD clinical spectrum.

Methods: A total of 136 subjects participated in this study. Four groups were established based on AD biomarker positivity from positron emission tomography (amyloid [A] and tau [T]) and clinical diagnosis (cognitively normal [CN] and impaired [IM]). CVD markers were derived from structural and quantitative magnetic resonance imaging data.

Results: Transcapillary pulse wave delay was significantly longer in controls compared to AT biomarker-confirmed groups (A+/T-/CN P < .001, A+/T+/CN P < .001, A+/T+/IM P = .003). Intracranial low-frequency oscillations were diminished in AT biomarker-confirmed groups both CN and impaired (A+/T-/CN P = .039, A+/T+/CN P = .007, A+/T+/IM P = .011). A significantly higher presence of microhemorrhages was measured in A+/T+/CN compared to controls (P = .006).

Discussion: Cerebrovascular markers indicate increased vessel stiffness and reduced vasomotion in AT biomarker-positive subjects during preclinical AD.

Keywords: Alzheimer's disease; amyloid imaging; low‐frequency oscillations; vascular imaging; vessel stiffness.

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Conflict of interest statement

K.A. Cody, T. Reher, and R.V. Cadman have nothing to disclose. L.A. Rivera‐Rivera's effort on this work was supported by the Alzheimer's Association AARFD‐20‐678095 paid to the institution. L. Eisenmenger's effort on this work was supported by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR002373 and KL2TR002374, as well as the Wisconsin Alzheimer's Disease Research Center grant P30‐AG062715 paid to the institution. T. Betthauser has also received grant support outside the submitted work from Alzheimer's Association AARF‐19‐614533 paid to the institution. H.A. Rowley has received consulting fees from GE HealthCare, iSchemaView and for accredited lectures and podcasts for Northwest Imaging Forums and the Data Safety Monitoring Board of HL Gore. H.A. Rowley has also received grant support outside the submitted work from the Radiological Society of North America, Fellow Grant paid to the institution. C.M. Carlsson has received payments for her participation in an NIH study at UCLA (D‐CARE) and study drugs from Amarin Corporation (Vascepa and placebo) for VA Merit Study. C.M. Carlsson has also received grant support outside the submitted work from NIH/Lilly, NIH/Esai, VA Merit, and NIH to the institution. N.A. Chin has received payments for being a member of the Med‐Sci committee for the WI Alzheimer's Association and member of the Med‐Sci committee for the Alzheimer's Foundation of America. S.C. Johnson serves on an advisory board for Roche Diagnostics for which he receives an honorarium and is principal investigator of an equipment grant from Roche. He receives research funding from Cerveau Technologies. K.M. Johnson has received royalties for patents licensed by the institution (UW‐Madison) unrelated to this project. K.M. Johnson has also received grant support outside the submitted work from NIH and GE Healthcare as research support to the institution.

Figures

FIGURE 1
FIGURE 1
Blood flow and transcapillary pulse wave delay from cardiac‐resolved 4D flow magnetic resonance imaging. Data are summarized for controls and amyloid/tau (AT) biomarker–confirmed groups (amyloid positive [A+], tau positive [T+], cognitively normal [CN], cognitively impaired [IM]). A, Cerebral blood flow (basilar [BA] + internal carotid artery [ICA]) was normalized to brain volume and was similar between groups. B, ICA and BA transcapillary pulse wave delay time was significantly longer in the control group (A–/T–/CN) compared to A+/T–/CN (ICA P < .001; BA P = .001), A+/T+/CN (ICA P < .001; BA P = .050) and (A+/T+/IM; ICA P = .003, BA P = .026) indicating a significantly faster transmission of the cardiac pulse wave in the intracranial vascular network of AT biomarker–confirmed groups
FIGURE 2
FIGURE 2
Low‐frequency flow range and standard deviations from time‐resolved 4D flow magnetic resonance imaging (MRI). A, Low‐frequency flow range and (B) flow standard deviation (σ) measured from time‐resolved 4D flow MRI data are summarized for controls and amyloid/tau (AT) biomarker–confirmed groups (amyloid positive [A+], tau positive (T+), cognitively normal [CN], cognitively impaired [IM)]). Brain volume normalized low frequency flow range and flow σ were significantly lower in the arterial circulation (internal carotid artery [ICA]) in AT‐positive groups (A+/T–/CN, A+/T+/CN, A+/T+/IM) compared to controls (A–/T–/CN; flow range: P < .001, P < .001, P = .004; flow σ: P < .001, P < .001, P = .005) respectively. These results suggest diminished autoregulation‐related vasomotion in Alzheimer's disease biomarker‐confirmed groups. A similar trend of lower flow range and σ in AT biomarker–positive groups was observed in the venous circulation
FIGURE 3
FIGURE 3
Low‐frequency flow oscillation power spectrum density analysis. Group average power spectrum density low frequency oscillations (LFOs) derived from time‐resolved 4D flow data in the internal carotid artery (ICA) (A) and superior sagittal sinus (SSS) (B) for tau negative (A–/T–/CN, A+/T–/CN) and tau positive groups (A+/T+/CN, A+/T+/IM; cognitively normal [CN], cognitively impaired [IM]). Diminished frequency content in ICAs and SSS was measured in biomarker‐positive groups compared to controls. ICAs’ autoregulation‐related LFO content [0.003, 0.100 Hz] was significantly reduced in these groups compared to controls (P = .039 A+/T–/CN; P = .007 A+/T+/CN; P = .011 A+/T+/IM). The threshold for statistical significance was P < .05
FIGURE 4
FIGURE 4
Linear regression modeling of dynamic vascular markers and amyloid/tau (AT) biomarkers. Linear regressions with 95% confidence intervals showing internal carotid artery (ICA) transcapillary pulse wave delay (A, B) and low‐frequency oscillations (LFOs; C, D) as a function of global Pittsburg compound B (PiB) distribution volume ratio (DVR) and enthorinal cortex MK‐6240 standardized uptake value ratio (SUVR) in all participants (n = 136). Linear correlations were relatively weak with coefficients of R2 = 0.12 (transcapillary pulse wave delay vs. PiB DVR), R2 = 0.04 (MK‐6240 SUVR) and R2 = 0.11 (LFOs vs. PiB DVR), R2 = 0.07 (MK‐6240 SUVR). Vertical lines mark global PiB DVR threshold > 1.19 for A+ (A, C) and entorhinal cortex MK‐6240 SUVR threshold > 1.27 for T+ (B, D)
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