Is dysregulation of cerebral perfusion an early marker and cause of MCI and AD?: Targeting cerebrovascular pathology in Alzheimer's disease: The pros and cons of intensive blood pressure lowering treatment in AD. (7th December 2020)

• Record Type: Journal Article
• Authors: Marmarelis, Vasilis; Shin, Dae; Zhang, Rong
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 2
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• 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.041947
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Our previous work has shown that the dynamic vasomotor CO2 reactivity of cerebral vasculature is significantly reduced (p<0.01) in amnestic MCI patients relative to age‐matched controls [Marmarelis et al., J. Alzh. Dis . 56:89–105, 2017]. This quantitative result was obtained through dynamic modeling of the effects of spontaneous changes of arterial blood pressure (ABP) and end‐tidal CO2 (etCO2) upon cerebral blood flow or cortical tissue oxygenation (CTO) measured via Near‐Infrared Spectroscopy at the lateral prefrontal cortex. This study incorporated the dynamic effects of additional contemporaneous changes of heart‐rate (HR), respiratory‐rate (RR) and tidal‐volume (TV) to examine whether and how the intertwined dynamics of these complex mechanisms of physiological regulation influence differentially the cerebral/cortical perfusion in MCI patients versus age‐matched controls. Method: We employed our novel multi‐variate predictive modeling methodology to quantify the input‐output dynamic relationship between contemporaneous changes of CTO (output) and five input variables: ABP, etCO2, HR, RR and TV, under spontaneous resting conditions. The obtained subject‐specific input‐output predictive models were used to compute indices that quantify relevant physiological mechanisms and compare differences between MCI patients (MP) and control subject (CS). Result: The obtained model‐based indices were significantly different for MP vs. CS with regard to the dynamic relationships between CTO and etCO2 or TV changes. This is illustrated in Figure 1, where the average model‐kernels (representing the CTO response to a unit‐impulse change of etCO2 or TV) are shown for 40 MP (red line) and 15 CS (blue line), along with SD bounds (dashed lines). Improved delineation between MP and CS resulted (p<0.001) when the obtained model‐based indices of Dynamic Vasomotor CO2 Reactivity (DVCR) and Dynamic Cortical Oxygenation Reactivity (DCOR), respectively, were combined in a composite index defined by the Fisher Discriminant of the respective scatter‐plot (see Figure 2). Conclusion: Dysregulation of cerebral/cortical perfusion can be detected early and quantified through dynamic predictive modeling of spontaneous hemodynamic/respiratory time‐series data, allowing delineation between MCI patients and controls (p< 0.001) for improved MCI diagnosis. This is viewed as an indication that such dysregulation of cerebral perfusion may be an early trigger/link in the pathogenic cascade towards neurodegenerative disease.