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Predicting the risk of progression in patients with thoracic aortic aneurysm 10.17863/CAM.47001 Hill, Priya Sinha, Sanjay 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. University of Cambridge 2020-01-01 Thesis Doctoral Doctor of Philosophy (PhD) en https://www.repository.cam.ac.uk/handle/1810/299932 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/5d7fa4af-fbc3-441a-922b-1d596f1f7087/download d7b36cb9f56271a11eeb6a1408cd4f5c https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/4c421c96-3aec-4568-98ea-50179690232e/download 47d9a3bcdd920c9a52102b338b205a30 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/dacab310-5b4e-453f-85b9-3a8f0de1833b/download 62f463733daaca4af56575fb1e55310a https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/3d6163ba-5b6f-4866-b00d-e0b5c1ab152c/download 87eda9de84448d1f82354d60eee3eb5f https://www.rioxx.net/licenses/all-rights-reserved/ Progression Aorta Aneurysm Biomarkers Wall stress