Determinants of White Matter Hyperintensity Burden Differ at the Extremes of Ages of Ischemic Stroke Onset

Abstract

Background and Purpose Age is a well-known risk factor for both stroke and increased burden of white matter hyperintensity (WMH), as detected on MRI scans. However, in patients diagnosed with ischemic stroke (IS), WMH volume (WMHv) varies significantly across age groups. We sought to examine the determinants of WMH burden across the ages of stroke onset with the goal to uncover potential age-specific stroke prevention targets. Methods Adult subjects from an ongoing hospital-based cohort study of IS patients with admission brain MRI were categorized as having early (<55 years), late (>75 years), or average (55–75 years) age of stroke onset. WMHv was measured using a previously validated, MRI-based semi-automated method and normalized for linear regression analyses. Results Of 1,008 IS subjects, 249 had early-onset stroke (24.7%) and 311 had late-onset stroke (30.9%). In multivariable analysis of WMHv using backward stepwise selection, only age (β=0.02, p=0.018), hypertension (β=0.24, p=0.049), and history of tobacco use (β=0.38, p=0.001) were independently associated with WMHv in patients with early-onset stroke, whereas male sex (β=−0.30,p=0.007), hyperlipidemia (β= −0.27, p=0.015), and current alcohol use (β=0.23, p=0.034) were independently associated with WMHv in patients with late-onset stroke. Conclusions History of tobacco use is a strong independent predictor of WMH burden in patients with early-onset stroke, while age is no longer associated with WMHv in IS patients older than 75 years. These findings suggest that the major risk factors to target for stroke prevention differ across age groups and may be modifiable.

Determinants of white matter hyperintensity burden in patients with Fabry disease

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Interaction of APOE e4 and poor glycemic control predicts white matter hyperintensity growth from 73 to 76

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White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts – The MRI-GENIE Study

“…It uses the ReLu activation function on all convolution layers. We optimize the parameters of the neural network using an independent set of manual WMH outlines (Zhang et al, 2015; 699/91/90 outlines used for training/validating/testing) via stochastic updates with the Adadelta optimizer (Zeiler, 2012). Figure 3: Architecture for automated WMH segmentation.…”