Background
Stroke is defined as an abrupt neurological outburst caused by impaired perfusion through the blood vessels to the brain. It is a major cause of mortality and long-term disability worldwide. Globally, more than 100 million people have been reported to suffer from stroke [
1].
Stroke can be characterized by physiological brain changes which can be measured noninvasively using magnetic resonance imaging (MRI) [
2]. Indeed, many observational studies have been performed to explore the relationships between brain imaging-derived phenotypes (IDPs) and stroke. Researchers have tried to investigate whether IDPs could potentially affect the risk of stroke. For example, fractional anisotropy (FA) is a commonly used MRI measure of the anisotropic diffusion degree with values between one (intact white matter) and zero (disrupted white matter). A follow-up study for 4259 subjects found that lower FA increased the risk of stroke, independent of other risk factors [
3]. Several prospective studies [
4‐
6] conducted on a range of different populations found that white matter lesions are correlated with increased risk of stroke. In addition to studies in pre-stroke subjects, many studies have also used MRI to assess brain changes in patients after stroke. For example, in post-stroke patients, reduced FA within the ischemic lesion or in the corticospinal tract remote from the lesion was observed [
7‐
9]. Moreover, lower FA values were associated with greater motor deficit and worse motor recovery [
10,
11]. However, conventional observational studies are more likely influenced by residual confounding. Therefore, whether the associations between IDPs and stroke are causal is uncertain.
Mendelian randomization (MR) is a useful genetic epidemiology study design using genetic variants as instrumental variables (IVs) to investigate whether the exposure is causally related to a medically relevant disease risk [
12]. Since inherent genetic variants are not susceptible to environmental variables, the MR design can avoid the potential confounding factors that are common in conventional observational studies [
13]. In this study, we used two-sample MR to systematically investigate the bidirectional causal associations between 587 IDPs and stroke. Analyses were performed for 5 stroke types, including any stroke (AS), which comprises ischemic stroke, intracerebral hemorrhage, and stroke of undetermined type; any ischemic stroke (AIS) regardless of subtype; and three ischemic stroke subtypes: large-artery atherosclerotic stroke (LAS), cardioembolic stroke (CES), small-vessel stroke (SVS). We chose these 5 types according to the common etiological subtypes of stroke [
14]. Our results might offer new insights into the prevention, diagnosis and treatment for stroke.
Discussion
Observational studies have reported that IDPs are associated with stroke; however, whether the relationships are causal is uncertain. In the present study, we performed bidirectional two-sample MR analyses to systematically investigate the causal associations between 587 IDPs and stroke. We identified 14 IDPs with statistically significant evidence of potential causal effects on stroke. We also identified potential causal effects of stroke on one IDP of commissural fiber.
In the forward MR analysis, we observed that 14 IDPs were causally associated with stroke. These IDPs were derived from projection or association fibers. Of note, these IDPs included 9 MD values and 1 FA value in different regions. As we mentioned in the results section, both measures are generally used to track structural integrity. Increased MD and decreased FA will be observed in damaged structurally organized tissue (e.g., white matter tracts). The MR results showed that higher genetically determined MD (or lower genetically determined FA) was causally associated with increased risk of AS, suggesting that disrupted integrity of the projection or association fibers is a potential risk factor for stroke. Changes in white matter microstructural integrity have been shown to precede irreversible white matter lesions [
38], which has been widely reported as an important risk factor for stroke [
4‐
6]. A recent study reported that white matter lesions showed significantly lower blood flow, blood volume, and capillary metabolic rate of oxygen [
39]. This might increase the risk of thrombus and further lead to ischemic stroke which typically begins with an acute phase in which ischemia results from a thrombus that lodges in a cerebral blood vessel. Further studies are needed to clarify the underlying physiological mechanism. Consistent with our results, a population-based study found that both lower FA and higher MD increased risk of stroke, independent of other risk factors [
3]. Another study carried out in a longitudinal cohort of 800 community-dwelling adults found that lower FA was associated with higher risk of stroke [
40]. Taking white matter lesions into consideration could improve the performance of stroke risk prediction models [
41].
The forward MR results showed that some IDPs were only associated with specific stroke subtypes. For instance, MD in the right external capsule was only positively associated with SVS. Consistent with our results, a previous study [
42] has observed that compared with healthy controls, MD value in external capsule of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL, a common heritable cause of SVS) patients was significantly increased. White matter hyperintensities in this region or external capsule lesion in CADASIL patients were also reported in other studies [
43,
44]. However, reports about the association between IDPs derived from external capsule and other stroke types are still limited. These results indicated that IDPs in specific regions might have different effects on different stroke subtypes.
In reverse MR analysis, we observed genetically determined higher risk of AIS was associated with increased ISOVF in body of corpus callosum. Corpus callosum is the largest commissural structure consisting of white matter tracts that connect the cerebral hemispheres according to an anterior–posterior topographical organization [
45]. Higher ISOVF indicated increased extracellular water volume, which is expected in neuroinflammatory states [
46]. Therefore, this reverse MR result suggested that higher risk of ischemic stroke might lead to the sequential injury in corpus callosum. In ischemic stroke, the ischemic area turns into a necrotic core. Degeneration of the distal axons may occur both near and far from the ischemic bed [
47]. Consistent with our findings, a previous study [
48] has observed damage of fiber tracts with their accompanying myelin sheaths in the nonischemic corpus callosum in the middle cerebral artery occlusion rat model. Degeneration of corpus callosum has also been observed in post-stroke patients in population-based studies [
49,
50].
The significant results from the reverse MR were less than the forward MR analysis. One possible reason is that the variance explained by the IVs for IDPs was up to 7.03%, while the variance explained by the IVs for stroke was up to 2.08%. Since we have already used the GWAS data with the largest sample size to date, we cannot add more IVs in our current study. We believe that if more IVs are available in future larger scale GWAS for stroke, more results in the reverse MR analysis might be obtained. We used conservative Bonferroni corrections for multiple testing to keep our results robust. Under such circumstances, although all our MR results can find some similar evidence from previous case-control studies, some previously reported significant causal relationships may not remain in our study. For example, a previous study found that presence of brain infarcts was associated with a smaller hippocampus in elderly participants [
51], which were not found in our study. This might because the IDPs we used were subdivided into the left and right hemispheres and that previous study focused on elderly people. In addition, when applied to clinical interventions, the estimated MR effect size must be treated cautiously. The desirable application of predictive results to clinical and health care decisions depends on the effect size of the exposure on the outcome.
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