scholarly journals Cooccurrence of vascular risk factors and late-life white-matter integrity changes

2015 ◽  
Vol 36 (4) ◽  
pp. 1670-1677 ◽  
Author(s):  
Pauline Maillard ◽  
Owen T. Carmichael ◽  
Bruce Reed ◽  
Dan Mungas ◽  
Charles DeCarli
2011 ◽  
Vol 31 (2) ◽  
pp. 119-125 ◽  
Author(s):  
Miika Vuorinen ◽  
Alina Solomon ◽  
Suvi Rovio ◽  
Lasse Nieminen ◽  
Ingemar Kåreholt ◽  
...  

Neurology ◽  
2015 ◽  
Vol 84 (11) ◽  
pp. 1128-1135 ◽  
Author(s):  
Rui Wang ◽  
Laura Fratiglioni ◽  
Erika J. Laukka ◽  
Martin Lövdén ◽  
Grégoria Kalpouzos ◽  
...  

2011 ◽  
Vol 52 (3) ◽  
pp. e117-e122 ◽  
Author(s):  
H. Kimm ◽  
P.H. Lee ◽  
Y.J. Shin ◽  
K.S. Park ◽  
J. Jo ◽  
...  

2010 ◽  
Vol 81 (9) ◽  
pp. 1028-1032 ◽  
Author(s):  
P. van Vliet ◽  
R. G. J. Westendorp ◽  
D. van Heemst ◽  
A. J. M. de Craen ◽  
A. M. Oleksik

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Anne-Katrin Giese ◽  
Markus D Schirmer ◽  
Adrian V Dalca ◽  
Ramesh Sridharan ◽  
Lisa Cloonan ◽  
...  

Introduction: White matter hyperintensity (WMH) is a highly heritable trait and a significant contributor to stroke risk and severity. Vascular risk factors contribute to WMH severity; however, knowledge of the determinants of WMH in acute ischemic stroke (AIS) is still limited. Hypothesis: WMH volume (WMHv) varies across AIS subtypes and is modified by vascular risk factors. Methods: We extracted WMHv from the clinical MRI scans of 2683 AIS subjects from the MRI-Genetics Interface Exploration (MRI-GENIE) study using a novel fully-automated, volumetric analysis pipeline. Demographic data, stroke risk factors and stroke subtyping for the Causative Classification of Stroke (CCS) were performed at each of the 12 international study sites. WMHv was natural log-transformed for linear regression analyses. Results: Median WMHv was 5.7cm 3 (interquartile range (IQR): 2.2-12.8cm 3 ). In univariable analysis, age (63.1 ± 14.7 years, β=0.04, SE=0.002), prior stroke (10.2%, β=0.66, SE=0.08), hypertension (65.4%, β=0.75, SE=0.05), diabetes mellitus (23.1%, β=0.35, SE=0.06), coronary artery disease (17.6%, β=0.04, SE=0.002), and atrial fibrillation (14.6%, β=0.48, SE=0.07) were significant predictors of WMHv (all p<0.0001), as well as smoking status (52.2%, β=0.15, SE=0.05, p=0.005), race (16.5% Non-Caucasian, β=0.25, SE=0.07) and ethnicity (8.2% Hispanic, β=0.30, SE=0.11) (all p<0.01). In multivariable analysis, age (β=0.04, SE=0.002), prior stroke (β=0.56, SE=0.08), hypertension (β=0.33, SE=0.05), smoking status (β=0.16, SE=0.05), race (β=0.42, SE=0.06), and ethnicity (β=0.34, SE=0.09) were independent predictors of WMHv (all p<0.0001), as well as diabetes mellitus (β=0.13, SE=0.06, p=0.02). WMHv differed significantly (p<0.0001, unadjusted) across CCS stroke subtypes: cardioembolic stroke (8.0cm 3 , IQR: 4.2-15.4cm 3 ), large-artery stroke (6.9cm 3 , IQR: 3.1-14.7cm 3 ), small-vessel stroke (5.8cm 3 , IQR: 2.5-13.5cm 3 ), stroke of undetermined (4.7cm 3 , IQR: 1.6-11.0cm 3 ) or other (2.55cm 3 , IQR: 0.9-8.8cm 3 ) causes. Conclusion: In this largest-to-date, multicenter hospital-based cohort of AIS patients with automated WMHv analysis, common vascular risk factors contribute significantly to WMH burden and WMHv varies by CCS subtype.


2013 ◽  
Vol 333 ◽  
pp. e254-e255
Author(s):  
A. Amintaeva ◽  
M. Kravchenko ◽  
O. Andreeva ◽  
Y. Varakin ◽  
G. Gornostaeva ◽  
...  

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