@article{0de204432f0e40c78b23cd50796a491c,
title = "Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study",
abstract = "Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.",
author = "Kathleen Gilbert and Wenjia Bai and Charlene Mauger and Pau Medrano-Gracia and Avan Suinesiaputra and Lee, {Aaron M.} and Sanghvi, {Mihir M.} and Nay Aung and Piechnik, {Stefan K.} and Stefan Neubauer and Petersen, {Steffen E.} and Daniel Rueckert and Young, {Alistair A.}",
note = "Funding Information: This research has been conducted using the UK Biobank Resource under application 2964. The authors wish to thank all UK Biobank participants and staff. Funding was provided by British Heart Foundation (PG/14/89/31194), and by the National Institutes of Health (USA) 1R01HL121754. S.N. acknowledges the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at The Oxford University Hospitals Trust at the University of Oxford, and the British Heart Foundation Centre of Research Excellence. A.Y. acknowledges the Health Research Council of New Zealand (grants 17/234 and 17/608). A.L. and S.E.P. acknowledge support from the NIHR Barts Biomedical Research Centre and from the “SmartHeart” EPSRC program grant (EP/P001009/1). N.A. is supported by a Wellcome Trust Research Training Fellowship (203553/Z/Z). This project was enabled through access to the MRC eMedLab Medical Bioinformatics infrastructure, supported by the Medical Research Council (grant number MR/L016311/1). Publisher Copyright: {\textcopyright} 2019, The Author(s). Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",
year = "2019",
month = dec,
day = "1",
doi = "10.1038/s41598-018-37916-6",
language = "English",
volume = "9",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",
}
