AUTHOR=Jin Hui , Hou Jie , Qin Xue , Du Xingyue , Zheng Guangying , Meng Yu , Shu Zhenyu , Wei Yuguo , Gong Xiangyang 

TITLE=Predicting progression of white matter hyperintensity using coronary artery calcium score based on coronary CT angiography—feasibility and accuracy

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=15

YEAR=2023

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2023.1256228

DOI=10.3389/fnagi.2023.1256228

ISSN=1663-4365

ABSTRACT=<sec id="sec1"><title>Objective</title><p>Coronary artery disease (CAD) usually coexists with subclinical cerebrovascular diseases given the systematic nature of atherosclerosis. In this study, our objective was to predict the progression of white matter hyperintensity (WMH) and find its risk factors in CAD patients using the coronary artery calcium (CAC) score. We also investigated the relationship between the CAC score and the WMH volume in different brain regions.</p></sec><sec id="sec2"><title>Methods</title><p>We evaluated 137 CAD patients with WMH who underwent coronary computed tomography angiography (CCTA) and two magnetic resonance imaging (MRI) scans from March 2018 to February 2023. Patients were categorized into progressive (<italic>n</italic> = 66) and nonprogressive groups (<italic>n</italic> = 71) by the change in WMH volume from the first to the second MRI. We collected demographic, clinical, and imaging data for analysis. Independent risk factors for WMH progression were identified using logistic regression. Three models predicting WMH progression were developed and assessed. Finally, patients were divided into groups based on their total CAC score (0 to &lt;100, 100 to 400, and &gt; 400) to compare their WMH changes in nine brain regions.</p></sec><sec id="sec3"><title>Results</title><p>Alcohol abuse, maximum pericoronary fat attenuation index (pFAI), CT-fractional flow reserve (CT-FFR), and CAC risk grade independently predicted WMH progression (<italic>p</italic> &lt; 0.05). The logistic regression model with all four variables performed best (training: AUC = 0.878, 95% CI: 0.790, 0.938; validation: AUC = 0.845, 95% CI: 0.734, 0.953). An increased CAC risk grade came with significantly higher WMH volume in the total brain, corpus callosum, and frontal, parietal and occipital lobes (<italic>p</italic> &lt; 0.05).</p></sec><sec id="sec4"><title>Conclusion</title><p>This study demonstrated the application of the CCTA-derived CAC score to predict WMH progression in elderly people (≥60 years) with CAD.</p></sec>