Common associations of cerebral small vessel disease markers
Poster No:
228
Submission Type:
Abstract Submission
Authors:
Elizabeth Haddad1, Shayan Javid2, Neda Jahanshad2
Institutions:
1USC, Los Angeles, CA, 2University of Southern California, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
Cerebral small vessel disease (SVD) commonly occurs with aging, causes roughly ⅕ of strokes worldwide, and is frequently concomitant with neurodegenerative diseases including Alzheimer's disease (AD) – potentially exacerbating cognitive and physical manifestations of the disease (Wardlaw, 2013). Multimodal MRI can be leveraged to capture distinct features of SVD, including cerebral microbleeds (CMBs), white matter lesions (WMLs), subcortical infarcts, enlarged perivascular spaces, and brain atrophy. CMBs can be detected using T2*-GRE MRI sequences, such as susceptibility weighted imaging (SWI), which detects magnetic field distortions caused by products like iron (Haacke, 2004). CMBs are thought to reflect hemosiderin-laden macrophages, which are in close proximity to structurally abnormal blood vessels, like those seen in SVD (Martinez-Ramirez, 2014). T2w sequences, such as Fluid Attenuated Inversion Recovery (FLAIR), suppress free water CSF signal – effectively highlighting fluid extravasation and edema (Saranathan, 2017). WMLs are seen as hyperintense on FLAIR, particularly in the periventricular and deep white matter, and are presumed to be of vascular origin reflecting arteriolosclerosis, gliosis, demyelination, breakdown of the ventricular lining, and a reduction in periventricular vasculature – all of which can contribute to neurodegeneration (Dallaire-Théroux, 2017; Hase, 2018). The UK Biobank (Miller, 2016) is a large, densely phenotyped study on aging, allowing for detailed investigation into factors that influence SVD markers, which may contribute to neurodegenerative risk. Here, we assess demographic, disease, genetic, and lifestyle factors associations with CMBs and WMLs – two common markers of SVD.
Methods:
335 subjects (62.9±7.85 years old; 164F) with both SWI and FLAIR sequences available were visually inspected for the presence of CMB and WML. We used the MARS rating scale (Gregoire, 2009) to identify CMBs on SWI and validated our findings using other sequences to exclude CMB 'mimics'. FLAIR scans were used to detect the presence of periventricular and deep WMLs at any severity. (Figure 1a). Given the prominent association between deep WMLs and SVD, and the fact that all but 1 participant had some form of periventricular WML, we opted to investigate those subjects with at least two other markers of SVD (i.e. the presence of at least one CMB and the presence of deep WML), noted as CMB+WML throughout. Frequency and co-occurrence of factors such as demographic (sex, college attendance), disease presence, apoe4 carriage, and lifestyle (diet, sleep, measures of adiposity, smoking, alcohol) were assessed using association rule learning (ARL) (Agrawal, 1993). All results, including sets with more than 50% likelihood, that is, factors that clustered together to predict CMB+WML in more than 50% of cases, are reported.
Results:
37 (67.9±8.16 years old; 14F) subjects were found to have CMB+WML (11%). Subjects with CMB+WML tended to be older and more hypertensive (Figure 1b &1c). Factors that predicted CMB+WML 80% of the time included the co-occurrence of hypertension, male sex, coronary artery disease, overweight BMI, high waist to hip ratio, and suboptimal intake of whole grains and sugary foods. Sets that predicted CMB+WML across all ARL results and in those with greater than 50% likelihood are reported in Figure 2.
Conclusions:
We identified factors which co-occurred to predict the presence of multiple SVD pathologies. These results may help shed light on multiple risk factors acting synergistically to increase risk for SVD pathology and associated neurodegeneration. Future work will include identifying other markers of SVD, such as enlarged perivascular spaces, lacunes, and measures of brain atrophy to capture a complete picture of SVD load and its demographic, clinical, and lifestyle correlates.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging 2
Keywords:
Aging
Cerebrovascular Disease
MRI
White Matter
1|2Indicates the priority used for review
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