SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted Ageing, Blood Pressure, and Neurodegeneration: A Lifelong Concern that Dietary Magnesium may Help Address(Australian National University, 2024-06) Alateeq, Khawlah; Cherbuin, NicolasThe growing global burden of neurodegenerative diseases, cognitive decline, and dementia highlights the urgent need to identify modifiable risk factors that promote healthy brain ageing and slow the progression of dementia. This thesis investigates the impact of high blood pressure (BP) – a prevalent risk factor – on brain ageing. It also investigates the role dietary magnesium (Mg) can play in addressing this risk factor and promoting brain health. Five studies were conducted to address the objectives of this thesis. The initial study involved a comprehensive literature review to generate insights into the relationship between BP, magnesium, neurodegeneration, and cognitive decline within the context of ageing. This process involved summarizing the mechanisms and effects to enhance understanding of how these factors potentially interact in the ageing brain. This review generated essential background knowledge that the subsequent studies sought to build upon. In the second study, a systematic review and meta-analysis were performed to quantify the existing literature on the relationship between continuous BP and total/regional brain volumes and white matter lesions (WMLs). The systematic review revealed a significant association between higher BP, lower brain volumes, and larger WMLs across the entire range, extending beyond individuals with hypertension or pre-hypertension. This finding suggests that high BP is associated with poorer brain health in non-hypertensive individuals within a broader population. The third study built upon the findings of the second study, examining the impact of age, gender, and other risk factors on the association between BP and brain health. This investigation quantified the relationships between continuous BP levels, brain volumes, and WMLs while also considering various risk factors such as age, sex, BMI, and use of antihypertensive medication. The findings reveal a consistent association between BP, lower brain volumes, and larger WMLs across the entire BP range, even within the upper normal BP range. This association affects individuals of all ages, particularly younger adults, and exhibits stronger effects in women. Notably, lower BMI and the use of antihypertensive medication appear to exert a protective effect against the detrimental impact of BP on brain health. These findings have substantial implications for population health, suggesting that even minor increases in BP over a lifespan can significantly contribute to the overall disease burden. This underscores the importance of implementing early preventative measures to maintain optimal BP levels. The fourth and fifth studies explored the associations between Mg intake, brain volumes, and WMLs. Additionally, these studies investigated whether BP and inflammation mediate the neuroprotective effect of Mg. The findings from both the fourth and fifth studies demonstrate that high dietary Mg intake is associated with larger brain volumes and lower WMLs. These results imply that a diet rich in Mg could contribute to enhanced brain health and potentially decrease the risk of dementia in the general population. Surprisingly, the neuroprotective impact of Mg is found not to be mediated by BP levels. Instead, the neuroprotective effect of dietary Mg is partially mediated through inflammation. This supports the potential of Mg intake as an effective, scalable, and affordable intervention in reducing inflammation and protecting against premature brain ageing in the general population. In conclusion, this thesis highlights the significant impact of high BP on brain health and finds that dietary Mg intake yields a neuroprotective effect, which can be attributed to its anti- inflammatory properties. Overall, the findings paint an optimistic picture for general population health, emphasizing the potential benefits of early interventions targeting modifiable risk factors. Thus, sufficient support at the health policy, clinical, and community levels can help reduce or prevent certain known risks to brain health.18 0Item Restricted Deep Discourse Analysis for Early Prediction of Multi-Type Dementia(Saudi Digital Library, 2023-06-12) Alkenani, Ahmed Hassan A; Li, YuefengAgeing populations are a worldwide phenomenon. Although it is not an inevitable consequence of biological ageing, dementia is strongly associated with increasing age, and is therefore anticipated to pose enormous future challenges to public health systems and aged care providers. While dementia affects its patients first and foremost, it also has negative associations with caregivers’ mental and physical health. Dementia is characterized by irreversible gradual impairment of nerve cells that control cognitive, behavioural, and language processes, causing speech and language deterioration, even in preclinical stages. Early prediction can significantly alleviate dementia symptoms and could even curtail the cognitive decline in some cases. However, the diagnostic procedure is currently challenging as it is usually initiated with clinical-based traditional screening tests. Typically, such tests are manually interpreted and therefore may entail further tests and physical examinations thus considered timely, expensive, and invasive. Therefore, many researchers have adopted speech and language analysis to facilitate and automate its initial prescreening. Although recent studies have proposed promising methods and models, there is still room for improvement, without which automated pre-screening remains impracticable. There is currently limited empirical literature on the modelling of the discourse ability of people with prodromal dementia stages and types, which is defined as spoken and written conversations and communications. Specifically, few researchers have investigated the nature of lexical and syntactic structures in spontaneous discourse generated by patients with dementia under different conditions for automated diagnostic modelling. In addition, most previous work has focused on modelling and improving the diagnosis of Alzheimer’s disease (AD), as the most common dementia pathology, and neglect other types of dementia. Further, current proposed models suffer from poor performance, a lack of generalizability, and low interpretability. Therefore, this research thesis explores lexical and syntactic presentations in written and spoken narratives of people with different dementia syndromes to develop high-performing diagnostic models using fusions of different lexical and syntactic (i.e., lexicosyntactic) features as well as language models. In this thesis, multiple novel diagnostic frameworks are proposed and developed based on the “wisdom of crowds” theory, in which different mathematical and statistical methods are investigated and properly integrated to establish ensemble approaches for an optimized overall performance and better inferences of the diagnostic models. Firstly, syntactic- and lexical-level components are explored and extracted from the only two disparate data sources available for this study: spoken and written narratives retrieved from the well-known DementiaBank dataset, and a blog-based corpus collected as a part of this research, respectively. Due to their dispersity, each data source was independently analysed and processed for exploratory data analysis and feature extraction. One of the most common problems in this context is how to ensure a proper feature space is generated for machine learning modelling. We solve this problem by proposing multiple innovative ensemble-based feature selection pipelines to reveal optimal lexicosyntactics. Secondly, we explore language vocabulary spaces (i.e., n-grams) given their proven ability to enhance the modelling performance, with an overall aim of establishing two-level feature fusions that combine optimal lexicosyntactics and vocabulary spaces. These fusions are then used with single and ensemble learning algorithms for individual diagnostic modelling of the dementia syndromes in question, including AD, Mild Cognitive Impairment (MCI), Possible AD (PoAD), Frontotemporal Dementia (FTD), Lewy Body Dementia (LBD), and Mixed Dementia (PwD). A comprehensive empirical study and series of experiments were conducted for each of the proposed approaches using these two real-world datasets to verify our frameworks. Evaluation was carried out using multiple classification metrics, returning results that not only show the effectiveness of the proposed frameworks but also outperform current “state-of-the-art” baselines. In summary, this research provides a substantial contribution to the underlying task of effective dementia classification needed for the development of automated initial pre-screenings of multiple dementia syndromes through language analysis. The lexicosyntactics presented and discussed across dementia syndromes may highly contribute to our understanding of language processing in these pathologies. Given the current scarcity of related datasets, it is also hoped that the collected writing-based blog corpus will facilitate future analytical and diagnostic studies. Furthermore, since this study deals with associated problems that have been commonly faced in this research area and that are frequently discussed in the academic literature, its outcomes could potentially assist in the development of better classification models, not only for dementia but also for other linguistic pathologies.18 0