{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/299932"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/299932","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Predicting the risk of progression in patients with thoracic aortic aneurysm","abstract":"This body of work has examined a range of easily quantifiable circulating biomarkers in a cohort of 50 patients with aneurysms of the arch and descending thoracic aorta. I have described within this thesis various obstacles (logistical and biological) to identifying biomarkers of aneurysm progression. Despite these challenges, I have created a unique dataset quantifying plasma proteins and PBMC-RNAs in a cohort of 50 AD patients with measurements of growth or clinical progression over 2 years. Analysis of my dataset indicates that: a) aortic size alone is not a good predictor of growth b) prediction of future growth can be improved by combining plasma protein measurements with aortic size: dAoD/dT = 0.43(baseline indexed AoD) -1.5(logTGFB1) -0.86(logIL2) dAoV/dT = 1.57(baseline indexed AoD) +0.01(logTGFB1) -22.5(logIL2) c) wall stress estimations appear to provide even more robust predictions of future growth d) PBMC-RNA profiling could be useful as a diagnostic tool to detect patients with AD aneurysms.","abstract_html":"This body of work has examined a range of easily quantifiable circulating biomarkers in a cohort of 50 patients with aneurysms of the arch and descending thoracic aorta. I have described within this thesis various obstacles (logistical and biological) to identifying biomarkers of aneurysm progression. Despite these challenges, I have created a unique dataset quantifying plasma proteins and PBMC-RNAs in a cohort of 50 AD patients with measurements of growth or clinical progression over 2 years. 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