O Papel do Metaboloma no Acidente Vascular Cerebral Isquémico

• Autor(a) principal: Ana Beatriz da Costa Baptista
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/156182
• topic: Ciências médicas e da saúde; Medical and Health sciences

Stroke is a serious health challenge being the second largest cause of death worldwide. Mounting evidence suggests a connection between the microbiome and stroke via the gut-brain axis. Gut microbiota (GM) has been shown to play a role in stroke onset by affecting inflammatory and immune responses through its metabolites. Moreover, several studies have linked GM to stroke severity and post-stroke cognitive impairment. Most of the evidence results from experimental animal models posing the need for human studies development. Therefore, we aim to uncover microbial profiles and biomarkers in stroke patients during the acute and recovery phases. From February to December 2022, patients admitted to the Stroke Unit of the Centro Hospitalar Universitário de São João in Porto, who met the inclusion criteria, were recruited by signing informed consent. We conducted a longitudinal cohort study involving 30 ischemic and haemorrhagic stroke patients. Faecal, urine and blood samples were collected at two time points: at hospital admission (T0) and three months after stroke onset (T1). Gut microbiota, comprising bacteria and fungi profiles, was characterised by 16S rRNA sequencing based on the V3-V4 and ITS regions, respectively. Microbial metabolites were assessed in urine and plasma samples by proton nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS). Moreover, we monitored the patient's functional status using the modified Rankin Scale (mRS) and collected clinical parameters, including risk factors such as hypertension, dyslipidaemia, atherosclerosis, and previous history of stroke. A multi-block data strategy will be employed to analyse the longitudinal data, which involves breaking the data into distinct blocks based on their unique characteristics. By leveraging the longitudinal nature of the study, the data will be analysed in a way that integrates the information across different blocks. In addition, a network association approach will be used to explore the relationships between the different data features, allowing for a more comprehensive understanding of the complex interactions and associations within the data (clinical, microbiota and microbiome). This approach will enable us to gain valuable insights into the underlying patterns and mechanisms that drive the microbiota/microbiome changes observed over time. Our study will allow us to characterise microbiome dysbiosis associated with stroke and establish a correlation between microbiome profile with functional recovery and cognition after stroke. Furthermore, it will provide insight into gut-brain axis biomarkers that can aid in improving stroke recovery and management, and prevent its recurrence under a progression towards a "healthy microbiome".