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The intersection of Sleep Disorders, Cerebrovascular Disorders, and Cognitive Function: Exploring the Mechanisms and Management of Vascular Dementia
Other Title
The intersection of Sleep Disorders, Cerebrovascular Disorders, and Cognitive Function: Exploring the Mechanisms and Management of Vascular Dementia
Type
thesis
Date Issued
2023-07-15
Author(s)
TRAN THANH DUY LINH
Advisor
胡朝榮 ; 楊志豪
Subjects
系所名稱：國際醫學研究博士學位學程
Description
學位別：博士
語文別：英文
口試委員：張芳嘉 CHANG, FANG-CHIA;湯頌君 TANG, SUNG-CHUN ;徐榮隆 HSU, JUNG-LUNG ;陳兆煒 JOWY TANI ;胡朝榮 HU, CHAUR-JONG;劉文德 LIU, WEN-TE;楊志豪 YANG, CHIH-HAO
授權範圍：網際網路,開放日期為2023-07-15
語文別：英文
口試委員：張芳嘉 CHANG, FANG-CHIA;湯頌君 TANG, SUNG-CHUN ;徐榮隆 HSU, JUNG-LUNG ;陳兆煒 JOWY TANI ;胡朝榮 HU, CHAUR-JONG;劉文德 LIU, WEN-TE;楊志豪 YANG, CHIH-HAO
授權範圍：網際網路,開放日期為2023-07-15
Abstract
Background: Vascular dementia (VaD) is a significant global health issue, characterized by impaired cognitive functions. Its complex etiology or risk factors, intertwining with several factors such as obstructive sleep apnea (OSA), limits the effectiveness of existing therapeutic strategies. In addition, the incidence of mix-type (mixed) dementia, a condition in which Alzheimer's disease (AD) and VaD occur simultaneously is increasing along with aging.
Objectives: This thesis aimed to uncover the intersection of sleep disorders, cerebrovascular diseases, AD pathology and cognitive decline in the context of VaD. Our objectives were 1) to analyze pharmacotherapy trials targeting VaD, shedding light on the trajectory of preference treatment, 2) to develop a machine learning algorithm for early detection of sleep apnea in OSA patients under continous positive airway pressure (CPAP) therapy, potentially mitigating VaD-associated risk factors, and 3) to explore the impact of chronic cerebral hypoperfusion (CCH) in mixed dementia and elucidate the underpinning mechanisms.
Methods: We conducted a comprehensive literature review on pharmacotherapy for VaD, developed a machine learning model utilizing ECG signals from OSA patients, and used a CCH-induced model on wild type or transgenic AD mice to investigate underlying mechanisms of mixed dementia.
Results:
In the analysis of pharmacotherapy trials targeting VaD, we observed a trajectory towards the development of therapies that address multiple underlying pathophysiologies.
In the development of our machine learning algorithm, our results indicated that Support Vector Machine best performed across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and F1-score of 0.93. Segments 60 seconds before sleep events seemed to exhibit better performance than other pre-OSA segments.
Through our investigation of the impact of CCH on mixed dementia using a CCH-induced mice model, we observed significant cognitive decline in mixed dementia compared to VaD and AD alone mice models. Interestingly, this decline was not accompanied by a proportional increase in amyloid accumulation, suggesting the involvement of alternative mechanisms in cognitive impairment. We further investigated pro-inflammatory microglia and differential neuro-inflammation-related gene expression revealed potential contributors to cognitive decline in mixed dementia, emphasizing the importance of neuroinflammatory in this scenario.
Conclusions:
This thesis contributes to the understanding of VaD by examining the intersection of sleep disorders, cerebrovascular diseases, and cognitive decline. The analysis of pharmacotherapy trials highlights the importance of targeting multiple pathophysiologies and provides insights into the evolving landscape of VaD treatment. Our developed machine learning algorithm for early detection of sleep apnea in OSA patients offers a potential tool for mitigating VaD-associated risk factors. Furthermore, our investigation of cognitive decline in mixed dementia, focusing on CCH, highlighted the involvement of neuro-inflammation instead of amyloid pathology in VaD.
By addressing these objectives, this thesis enhances our knowledge of VaD and opens for the development of more effective prevention and treatment strategies. Further research is warranted to validate and expand upon these findings, ultimately improving the lives of individuals affected by VaD and related conditions.
Objectives: This thesis aimed to uncover the intersection of sleep disorders, cerebrovascular diseases, AD pathology and cognitive decline in the context of VaD. Our objectives were 1) to analyze pharmacotherapy trials targeting VaD, shedding light on the trajectory of preference treatment, 2) to develop a machine learning algorithm for early detection of sleep apnea in OSA patients under continous positive airway pressure (CPAP) therapy, potentially mitigating VaD-associated risk factors, and 3) to explore the impact of chronic cerebral hypoperfusion (CCH) in mixed dementia and elucidate the underpinning mechanisms.
Methods: We conducted a comprehensive literature review on pharmacotherapy for VaD, developed a machine learning model utilizing ECG signals from OSA patients, and used a CCH-induced model on wild type or transgenic AD mice to investigate underlying mechanisms of mixed dementia.
Results:
In the analysis of pharmacotherapy trials targeting VaD, we observed a trajectory towards the development of therapies that address multiple underlying pathophysiologies.
In the development of our machine learning algorithm, our results indicated that Support Vector Machine best performed across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and F1-score of 0.93. Segments 60 seconds before sleep events seemed to exhibit better performance than other pre-OSA segments.
Through our investigation of the impact of CCH on mixed dementia using a CCH-induced mice model, we observed significant cognitive decline in mixed dementia compared to VaD and AD alone mice models. Interestingly, this decline was not accompanied by a proportional increase in amyloid accumulation, suggesting the involvement of alternative mechanisms in cognitive impairment. We further investigated pro-inflammatory microglia and differential neuro-inflammation-related gene expression revealed potential contributors to cognitive decline in mixed dementia, emphasizing the importance of neuroinflammatory in this scenario.
Conclusions:
This thesis contributes to the understanding of VaD by examining the intersection of sleep disorders, cerebrovascular diseases, and cognitive decline. The analysis of pharmacotherapy trials highlights the importance of targeting multiple pathophysiologies and provides insights into the evolving landscape of VaD treatment. Our developed machine learning algorithm for early detection of sleep apnea in OSA patients offers a potential tool for mitigating VaD-associated risk factors. Furthermore, our investigation of cognitive decline in mixed dementia, focusing on CCH, highlighted the involvement of neuro-inflammation instead of amyloid pathology in VaD.
By addressing these objectives, this thesis enhances our knowledge of VaD and opens for the development of more effective prevention and treatment strategies. Further research is warranted to validate and expand upon these findings, ultimately improving the lives of individuals affected by VaD and related conditions.