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Impact of cardiovascular risk factors on atlas-based left ventricular shape phenotypes

Session Poster Session 4

Speaker Nay Aung

Event : ESC Congress 2018

  • Topic : imaging
  • Sub-topic : Cardiac Magnetic Resonance: Dimensions, Volumes and Mass
  • Session type : Poster Session

Authors : N Aung (London,GB), K Gilbert (Auckland,NZ), A Suinesiaputra (Auckland,NZ), A Lee (London,GB), MM Sanghvi (London,GB), F Zemrak (London,GB), K Fung (London,GB), JM Paiva (London,GB), E Lukaschuk (Oxford,GB), V Carapella (Oxford,GB), YJ Kim (Oxford,GB), SK Piechnik (Oxford,GB), S Neubauer (Oxford,GB), AA Young (Auckland,NZ), SE Petersen (London,GB)

Authors:
N. Aung1 , K. Gilbert2 , A. Suinesiaputra2 , A. Lee1 , M.M. Sanghvi1 , F. Zemrak1 , K. Fung1 , J.M. Paiva1 , E. Lukaschuk3 , V. Carapella3 , Y.J. Kim3 , S.K. Piechnik3 , S. Neubauer3 , A.A. Young2 , S.E. Petersen1 , 1Queen Mary University of London, William Harvey Research Institute - London - United Kingdom , 2The University of Auckland, Department of Anatomy and Medical Imaging - Auckland - New Zealand , 3University of Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine - Oxford - United Kingdom ,

Citation:
European Heart Journal ( 2018 ) 39 ( Supplement ), 772-773

Background: Mathematical models (or atlases) characterising a coordinate map registered to the cardiac anatomy have recently gained prominence due to their ability to represent global sub-clinical changes, and the improved statistical power to detect subtle differences.

Purpose: To examine the association between traditional cardiovascular risk factors and global LV shape variation derived from cardiovascular magnetic resonance images.

Methods: Figure 1 outlines the steps taken to create the LV atlas in the UK Biobank cohort. Principal component analysis was applied on 1682 LV surface coordinate points to extract the major modes of shape variation which is expressed in a normalised z-score. Multivariable linear regression was used to interrogate the relationship between each mode of variation in shape and cardiovascular risk factors.

Results: Age, dyslipidaemia and diastolic blood pressure (DBP) were associated with smaller global LV size while male gender, body mass index (BMI), systolic blood pressure (SBP), and exercise were associated with larger global LV size (Table 1). Higher BMI and SBP were correlated with a more spherical LV shape while the inverse relationship was found with ageing and higher DBP.

Conclusions: In a large middle-aged population free from known cardiovascular disease, traditional cardiovascular risk factors have a significant and sometimes opposing influence on atlas-derived LV shape phenotypes.

Table 1
N=4136 (46% male)Regression coefficient (Δz-score of each mode) [95% CI], adjusted for all other variables in the table
Risk factorComparisonED mode 1 (Size)ED mode 2 (Sphericity)ED mode 3 (MV inlet orientation)ES mode 1 (Size)ES mode 2 (Longitudinal shortening)ES mode 3 (Sphericity)
AgePer 10 years-0.23 [-0.28 to -0.17]**0.12 [0.07 to 0.18]**-0.15 [-0.21 to -0.09]**-0.21 [-0.27 to -0.16]**-0.18 [-0.23 to -0.12]**0.13 [0.07 to 0.18]**
SexMale vs Female0.38 [0.31 to 0.45]**0.02 [-0.05 to 0.09]-0.03 [-0.11 to 0.04]0.46 [0.39 to 0.52]**-0.44 [-0.51 to -0.38]**0.2 [0.13 to 0.27]**
EthnicityCaucasian vs Non-Caucasian-0.004 [-0.19 to 0.18]-0.18 [-0.37 to 0.01]0.18 [-0.02 to 0.38]0.001 [-0.18 to 0.18]-0.33 [-0.52 to -0.15]*-0.12 [-0.32 to 0.07]
Body mass indexPer SD (4.3 kg/m2)0.25 [0.21 to 0.28]**-0.19 [-0.23 to -0.16]**0.1 [0.07 to 0.14]**0.2 [0.16 to 0.23]**-0.12 [-0.16 to -0.09]**-0.01 [-0.05 to 0.03]
Systolic blood pressurePer SD (18.1 mmHg)0.22 [0.17 to 0.27]**-0.16 [-0.21 to -0.1]**0.02 [-0.04 to 0.07]0.15 [0.1 to 0.2]**-0.02 [-0.07 to 0.03]0.04 [-0.01 to 0.09]
Diastolic blood pressurePer SD (10.0 mmHg)-0.14 [-0.19 to -0.09]**0.16 [0.11 to 0.21]**-0.03 [-0.08 to 0.03]-0.07 [-0.12 to -0.02]*-0.01 [-0.06 to 0.04]0.04 [-0.01 to 0.09]
SmokingCurrent vs None-0.04 [-0.2 to 0.11]0.002 [-0.16 to 0.16]-0.09 [-0.26 to 0.08]-0.001 [-0.15 to 0.15]-0.06 [-0.22 to 0.1]0.1 [-0.06 to 0.26]
Regular alcohol use>3 units/wk vs <3 units/wk0.05 [-0.02 to 0.11]-0.08 [-0.15 to -0.01]-0.01 [-0.09 to 0.06]0.07 [0.004 to 0.13]0.003 [-0.06 to 0.07]-0.08 [-0.15 to -0.01]
ExercisePer SD (2840 MET min/wk)0.12 [0.09 to 0.15]**-0.06 [-0.09 to -0.03]*-0.0001 [-0.04 to 0.03]0.11 [0.08 to 0.14]**0.02 [-0.01 to 0.05]0.005 [-0.03 to 0.04]
DyslipidaemiaYes vs No-0.13 [-0.22 to -0.04]*0.12 [0.03 to 0.22]0.04 [-0.06 to 0.14]-0.12 [-0.2 to -0.03]*-0.08 [-0.17 to 0.01]0.1 [0.01 to 0.2]
Diabetes mellitusYes vs No-0.15 [-0.32 to 0.02]0.18 [0.004 to 0.36]0.02 [-0.17 to 0.2]-0.04 [-0.21 to 0.13]-0.03 [-0.2 to 0.14]0.13 [-0.05 to 0.31]
CI, confidence interval; ED, end-diastolic; ES, end-systolic; MV, mitral valve; SD, standard deviation; MET, metabolic equivalent of task. **p<0.0001; *p<0.001; p<0.05.
Figure 1. Creation of LV atlas

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