Left ventricular shape predicts different types of cardiovascular events in the general population.
To investigate whether sphericity volume index (SVI), an indicator of left ventricular (LV) remodelling, predicts incident cardiovascular events (coronary heart disease, CHD; all cardiovascular disease, CVD; heart failure, HF; atrial fibrillation, AF) over 10 years of follow-up in a multiethnic population (Multi-Ethnic Study of Atherosclerosis).



5004 participants free of known CVD had magnetic resonance imaging (MRI) in 2000-2002. Cine images were analysed to compute, [Formula: see text] equivalent to LV volume/volume of sphere with length of LV as the diameter. The highest (greatest sphericity) and lowest (lowest sphericity) quintiles of SVI were compared against the reference group (2-4 quintiles combined). Risk-factor adjusted hazard's ratio (HR) from Cox regression assessed the predictive performance of SVI at end-diastole (ED) and end-systole (ES) to predict incident outcomes over 10 years in retrospective interpretation of prospective data.



At baseline, participants were aged 61±10 years; 52% men and 39%/13%/26%/22% Cauc/Chinese/Afr-Amer/Hispanic. Low sphericity was associated with higher Framingham CVD risk, greater coronary calcium score and higher N-terminal pro-brain natriuretic peptide (NT-proBNP); while increased sphericity was associated with higher NT-proBNP and lower ejection fraction. Low sphericity predicted incident CHD (HR: 1.48, 1.55-2.59 at ED) and CVD (HR: 1.82, 1.47-2.27 at ED). However, both low (HR: 1.81, 1.20-2.73 at ES) and high (HR: 2.21, 1.41-3.46 at ES) sphericity predicted incident HF. High sphericity also predicted AF.



In a multiethnic population free of CVD at baseline, lowest sphericity was a predictor of incident CHD, CVD and HF over a 10-year follow-up period. Extreme sphericity was a strong predictor of incident HF and AF. SVI improved risk prediction models beyond established risk factors only for HF, but not for all CVD or CHD.
Johns Hopkins University, Baltimore, Maryland, USA.
Bharath
B
Ambale-Venkatesh
Johns Hopkins University, Baltimore, Maryland, USA.
Kihei
K
Yoneyama
Johns Hopkins University, Baltimore, Maryland, USA.
Ravi K
RK
Sharma
Johns Hopkins University, Baltimore, Maryland, USA.
Yoshiaki
0000-0002-6086-5191
Y
Ohyama
National Institutes of Health, Bethesda, Maryland, USA.
Colin O
CO
Wu
Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA.
Gregory L
GL
Burke
Columbia University, New York, New York, USA.
Steven
S
Shea
University of California at Los Angeles, Los Angeles, California, USA.
Antoinette S
AS
Gomes
University of Auckland, Auckland, New Zealand.
Alistair A
AA
Young
National Institutes of Health, Bethesda, Maryland, USA.
David A
DA
Bluemke
Johns Hopkins University, Baltimore, Maryland, USA.
João Ac
JA
Lima
D000368:Aged; D000369:Aged, 80 and over; D001281:Atrial Fibrillation / Q000208:ethnology / Q000503:physiopathology; D015415:Biomarkers / Q000097:blood; D002318:Cardiovascular Diseases / Q000208:ethnology* / Q000503:physiopathology; D003327:Coronary Disease / Q000208:ethnology / Q000503:physiopathology; D005260:Female; D006333:Heart Failure / Q000208:ethnology / Q000503:physiopathology; D006352:Heart Ventricles / Q000000981:diagnostic imaging* / Q000503:physiopathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D015994:Incidence; D053208:Kaplan-Meier Estimate; D019028:Magnetic Resonance Imaging, Cine; D008297:Male; D008875:Middle Aged; D020097:Natriuretic Peptide, Brain / Q000097:blood; D010446:Peptide Fragments / Q000097:blood; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D014481:United States / Q000453:epidemiology; D016277:Ventricular Function, Left; D020257:Ventricular Remodeling
D015415:Biomarkers; D010446:Peptide Fragments; C109794:pro-brain natriuretic peptide (1-76); D020097:Natriuretic Peptide, Brain