Multi‐center study of regulation of cerebral perfusion using spontaneous time‐series data shows impairment of CO2 vasoreactivity in MCI & AD patients. (December 2021)

• Record Type: Journal Article
• Authors: Marmarelis, Vasilis; Billinger, Sandra A.; Chui, Helena C.; Joe, Elizabeth B.; Shin, Dae; Cardim, Danilo; Hashem, Suhaib; Kaufman, Carolyn S.; Cullum, Munro; Kelley, Brendan; Zhang, Rong
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.054737
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Three AD Centers have joined together to study the regulation of cerebral perfusion at resting conditions using spontaneous time‐series data with a modeling methodology that previously yielded indications of reduced dynamic vasomotor CO2 reactivity in amnestic MCI patients [ Marmarelis et al., J. Alzh. Dis. 56:89–105, 2017 ] and significant correlation of this reduction with functional impairment of the chemoreflex in these MCI patients [ Marmarelis et al., J. Alzh. Dis. 75:1‐16, 2020 ]. The goal of this multi‐center study is to confirm these preliminary findings with larger cohorts of MCI patients, AD patients and age‐matched cognitively normal controls (NC), and to examine the time‐course of these impairments with longitudinal data over 5 years. Method: Quantitative results were obtained through dynamic modeling of the effects of spontaneous changes in arterial blood pressure (ABP) and end‐tidal CO2 (etCO2) upon cerebral blood flow velocity (CBFV) in the middle cerebral arteries measured via transcranial Doppler. The obtained subject‐specific input‐output predictive models were used to compute indices (physio‐markers) that quantify the Dynamic Vasomotor Reactivity (DVR) in each subject/patient and to compare differences between NC, and MCI/AD patients. Result: The obtained DVR indices were significantly different for patients (9 MCI and 4 AD taken together due to small numbers) vs. 32 controls (p=0.026). Notably, the delineation between patients and controls improved (p=0.009) after a session of slow paced‐breathing (8 breaths/minute) that led to larger average DVR for controls and to reduction of inter‐subject variability of DVR indices. This is illustrated in Figure 1, where the average model‐predicted responses (representing the CBFV response to a unit‐step change of etCO2) are shown for 13 MCI/AD patients (red line) and 32 controls (blue line) before and after the paced‐breathing session. Further improvement in delineating patients from controls is achieved (p=9x10 ‐5 ) when the DVR indices from the two sessions were averaged for each subject/patient. Conclusion: Significantly lower DVR index under resting spontaneous conditions was observed in 13 MCI/AD patients relative to 32 NC before (p= 0.026) and after (p=0.009) slow paced‐breathing session. Averaging of the DVR indices obtained from data before and after paced‐breathing improved substantially this delineation (p=9x10 ‐5 ).