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Received: November 8, 2023. Revised: December 8, 2023. Accepted: December 9, 2023
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Cerebral Cortex, 2024, 1–8
https://doi.org/10.1093/cercor/bhad518
Original Article
Genetic analyses identify brain imaging-derived
phenotypes associated with the risk of intracerebral
hemorrhage
Yi Liu1,†, Yiming Jia1,†,Hongyan Sun2,Lulu Sun1,Yinan Wang1, Qingyun Xu1,Yu He1,Xinyue Chang1, Daoxia Guo3,Mengyao Shi1,
Guo-Chong Chen4,Jin Zheng5,*, Zhengbao Zhu1,*
1Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory
of Geriatric Diseases and Immunology, 199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China,
2Department of Medical Imaging, 11 Guangqian Road, Xiangcheng District, The Aff iliated Guangji Hospital of Soochow University, Suzhou, China,
3School of Nursing, 333 Ganjiang East Road, Gusu District, Suzhou Medical College of Soochow University, Suzhou, China,
4Department of Nutrition and Food Hygiene, School of Public Health,199 Renai Road, Industrial Park District, Suzhou Medical College of Soochow University,
Suzhou, China,
5Department of Neurology, Minhang Hospital, 170 Xinsong Road, Xinzhuang Town, Minhang District, Fudan University, Shanghai, China
*Corresponding authors: Zhengbao Zhu, Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational
Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, 199 Renai Road, Industrial Park District, Suzhou, Jiangsu Province 215123, China.
Email: zbzhu@suda.edu.cn; Jin Zheng, Department of Neurology, Minhang Hospital, Fudan University, Shanghai, China. Email: Zhengjin22163@fudan.edu.cn
†Yi Liu and Yiming Jia contributed equally to this work.
Previous observational studies have reported associations between brain imaging-derived phenotypes (IDPs) and intracerebral hem-
orrhage (ICH), but the causality between them remains uncertain. We aimed to investigate the potential causal relationship between
IDPs and ICH by a two-sample Mendelian randomization (MR) study.We selected genetic instruments for 363 IDPs from a genome-wide
association study (GWASs) based on the UK Biobank (n=33,224). Summary-level data on ICH was derived from a European-descent
GWAS with 1,545 cases and 1,481 controls. Inverse variance weighted MR method was applied in the main analysis to investigate the
associations between IDPs and ICH. Reverse MR analyses were performed for significant IDPs to examine the reverse causation for the
identified associations. Among the 363 IDPs, isotropic or free water volume fraction (ISOVF) in the anterior limb of the left internal
capsule was identified to be associated with the risk of ICH (OR per 1-SD increase, 4.62 [95% CI, 2.18–9.81], P=6.63 ×10−5). In addition,
the reverse MR analysis indicated that ICH had no effect on ISOVF in the anterior limb of the left internal capsule (beta, 0.010 [95% CI,
−0.010-0.030], P=0.33). MR-Egger regression analysis showed no directional pleiotropy for the association between ISOVF and ICH, and
sensitivity analyses with different MR models further confirmed these findings. ISOVF in the anterior limb of the left internal capsule
might be a potential causal mediator of ICH, which may provide predictive guidance for the prevention of ICH. Further studies are
warranted to replicate our findings and clarify the underlying mechanisms.
Key words:brain imaging-derived phenotypes; stroke; intracerebral hemorrhage; biomarker; Mendelian randomization.
Introduction
Stroke is the second leading cause of death and the major cause
of long-term disability worldwide (Herpich and Rincon 2020), and
intracerebral hemorrhage (ICH) accounts for about 10% to 20% of
all stroke (An et al. 2017). In 2018, the incidence of ICH was 24.6
per 100,000 person-years, and the number of the patients with
ICH was expected to increase continually (Ziai and Carhuapoma
2018). Identification of novel markers is highly desirable for bet-
ter refining risk prediction and understanding the pathogenesis
of ICH.
Mounting evidence had suggested critical roles of brain
imaging-derived phenotypes (IDPs) in the diagnosis and treat-
ment of ICH. For instance, it has been shown that diffusion-
weighted imaging-hyperintensities in the cortex and subcortical
white matter was associated with a high predisposition to the
development of ICH (Kimberly et al. 2009). In terms of the
prognostic value, the presence of spot sign on magnetic resonance
imaging (MRI) was reported as an independent biomarker of
hematoma expansion and poor functional outcome for patients
with acute ICH (Valyraki et al. 2023). Similarly, noncontrast com-
puted tomography hypodensities could also provide additional
information for the risk stratification of hematoma expansion
after ICH (Morotti et al. 2023). Of note, the aforementioned
associations were obtained from observational studies, while
confounding and reverse-causality biases were difficult to avoid
in these studies (Lawlor et al. 2008). Recently, Nelson et al. (2015)
indicated that genomics had the potential to elucidate the causal
pathways linking phenotypes and diseases. Fortunately, recent
advancement in high-throughput genotyping and neuroimaging
techniques enabled genome-wide association study (GWAS) to
comprehensively reveal genetic determinants of IDPs (Smith et al.
2021). Such developments offered an opportunity to explore the
potential causal relationships between IDPs and ICH through
Mendelian randomization (MR) design.
MR is an emerging genetic epidemiological method using sin-
gle nucleotide polymorphisms (SNPs) related to the exposure as
instrumental variables to estimate the associations between the
exposures and the diseases of interest (Smith and Ebrahim 2003;
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2|Cerebral Cortex, 2024
Sanderson et al. 2022). Given that genetic variants are randomly
allocated during conception, residual confounding and reverse
causation biases could be minimized in MR studies (Davies et al.
2018). MR design has been previously applied to investigate the
associations between several modifiable factors and the risk of
ICH, including blood pressure (Georgakis et al. 2020), lipids (Yu
et al. 2022), vitamin D (Szejko et al. 2022), etc. However, the
potential causal associations of IDPs with ICH have not been
assessed via MR design. Herein, we conducted a two-sample MR
study to estimate the associations between 363 IDPs and ICH.
Methods
Study design
We aimed to identify brain-imaging predictors for ICH from the
genetic perspective in this two-sample MR study (Fig. 1). MR
studies are based on three main assumptions: (i) the genetic
instruments must be associated with the exposure; (ii) the
genetic instruments must be independent of confounders; and
(iii) and the genetic instruments must influence the outcome
only through the exposure. Summary-level data of IDPs and ICH
utilized in the present study were derived from publicly available
GWASs of European ancestry (Woo et al. 2014;Smith et al.
2021). The participant selection, participants’ characteristics, and
genotyping were described in detail in previous studies (Woo et al .
2014;Smith et al. 2021). The protocol and data collection were
approved by the ethics committee of the original GWASs, and
written informed consent was obtained from each participant
before data collection.
Data source and sample overlap
We obtained the summary statistics for 587 brain structural IDPs
(Guo et al. 2022) based on the GWAS dataset released by Smith
et al. (2021), involving 33,224 European individuals from the UK
Biobank (Supplementary Table 1). The neuroimaging measures
and processing methods were described in detail in the online
reference of UK Biobank (https://biobank.ctsu.ox.ac.uk/crystal/
crystal/docs/brain_mri.pdf). Summary statistics for ICH were
obtained from the International Stroke Genetics consortium’s
GWAS meta-analysis of six European-descent cohorts, including
1,545 cases and 1,481 controls. ICH cases were defined as
new and acute (<24 h) neurological deficits with presence of
intraparenchymal bleeding according to brain imaging.
Given that the potential overlap between participants in expo-
sure and outcome GWASs may lead to weak instrument bias
(Burgess et al. 2016), we assessed the bias from sample overlap
and the corresponding type I error rate through an online web
tool (https://sb452.shinyapps.io/overlap/)(Burgess et al. 2016).
Instrument selection
In this study, SNPs that had been identified to be associated with
IDPs at the genome-wide significance level (Pvalue <5×10−8)
and not in linkage disequilibrium (LD) with other SNPs (r2<0.1
within a clumping window of 500 kb) were selected as instru-
mental variables to proxy these IDPs. When we encountered
certain SNPs exhibiting LD above a threshold of r2= 0.1, only
the SNP with the lowest Pvalue for association with the IDP
was selected. Palindromic SNPs were excluded during the har-
monization process. We calculated the phenotypic variance of
each IDP explained by the corresponding instruments using the
package gtx in the statistical software R, and the calculation
formula had been previously described in detail (Dastani et al.
2012). To ensure the reliability of causal inference, we removed
the IDPs whose phenotypic variance explained by genetic variants
was <0.5% to reach a sufficient statistical power (Chong et al.
2019). Furthermore, we excluded the IDPs associated with less
than three SNPs to meet the minimum requirement of num-
ber of SNPs for some sensitivity analyses (Hemani et al. 2018)
(Supplementary Fig. 1).
According to the above exclusion criteria, 224 of the 587 IDPs
were ruled out. Finally, a total of 363 IDPs were retained for MR
analyses (Fig. 1). Overview of the SNPs used as genetic instru-
ments was listed in Supplementary Table 2.Wecalculatedthe
F-statistic to evaluate the strength of the genetic instruments
for IDPs. The F-statistic is an indicator of the extent (size and
probability) of the relative bias that is likely to occur in estimating
a causal relationship using the instrumental variables (Stock
et al. 2002), and the F-statistic greater than 10 suggests a strong
instrument (Brion et al. 2013).
Statistical analysis
In the main analysis, we adopted the inverse-variance weighted
(IVW) method to estimate the associations between 363 IDPs and
the risk of ICH (Lawlor et al. 2008). Heterogeneity among the
included SNPs was evaluated by Cochran’s Q statistic (Bowden
et al. 2016). A random-effect IVW model was used if the hetero-
geneity existed; otherwise, a fixed-effect IVW model was used. We
leveraged the online web tool (https://shiny.cnsgenomics.com/
mRnd/) to estimate the power for the MR analyses (Brion et al.
2013).
Given that IVW estimates were imprecise in the presence of
invalid instruments or pleiotropy, we further performed sensitiv-
ity analyses with the following MR methods to assess the robust-
ness of our findings: the penalized IVW method could penalize
the SNPs with pleiotropy (Xu et al. 2022); the maximum likelihood
method enabled us to make a valid estimation in the case of
measurement error in SNP-exposure association (Hemani et al.
2018); the MR pleiotropy residual sum and outlier (MR-PRESSO)
method was capable of identifying outlying SNPs and providing
reliable estimates with outlier correction (Ong and Macgregor
2019); the MR robust adjusted profile score (MR-RAPS) method
was robust to the violations of key MR assumption (Zhao et al.
2020); the leave-one-out method was utilized to test whether the
studied associations were driven by an individual SNP through
sequentially dropping each of them (Hemani et al. 2018); and
the MR-Egger regression was effective in reflecting the potential
pleiotropy by its intercept term (Hemani et al. 2018).
For IDPs identified to be associated with ICH, we further per-
formed MR analyses with ICH as the exposure and these IDPs
as the outcomes to determine the reverse causation between
significant IDPs and ICH. The instrument selection for ICH in the
reverse MR analyses followed the same criteria as that for IDPs
in the forward MR analyses. Reverse MR analyses used the same
analytical models as that in forward MR analyses.
In the forward MR analyses,the results for ICH are presented as
odds ratios (ORs) with their 95% confidence intervals (CIs), and a
2-sided P<1.38 ×10−4(Bonferroni-corrected significance thresh-
old calculated as 0.05/363) was considered statistically significant.
In the reverse MR analyses, the results for IDPs were presented as
betas with their 95% CIs, and a 2-sided P<0.05 was considered as
suggestive evidence for potential reverse causation. In addition,a
2-sided P<0.05 was considered as suggestive evidence for poten-
tial pleiotropy in the MR-Egger regression analyses. All analyses
were performed in R (version 3.4.3; R Development Core Team)
with the packages gtx, MendelianRandomization, MRPRESSO, and
TwoSampleMR.
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Liu et al. |3
Fig. 1. Conceptual workflow of this MR study. Abbreviations: GWAS, genome-wide association study; IDP, imaging-derived phenotype; IVW, inverse-
variance weighted; MR, Mendelian randomization; MR-PRESSO, MR pleiotropy residual sum and outlier; MR-RAPS, MR robust adjusted profile score;
SNP, single nucleotide polymorphism.
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4|Cerebral Cortex, 2024
Fig. 2. Circular Manhattan plot for the inverse-variance weighted MR estimates of the associations between IDPs and the intracerebral hemorrhage. The
black dashed line indicates the Bonferroni-corrected significance threshold (P<1.38 ×10−4), and the significant imaging-derived phenotype (IDP) were
annotated with labels. The deep color bar represents the significant IDP in MR analyses. According to the tissue/region listed in Supplementary Table 1,
we grouped the 363 IDPs. The detailed results are available in Supplementary Table 5. Abbreviation: ISOVF, isotropic or free water volume fraction.
Results
Strength of the genetic instruments for IDPs
A total of 363 IDPs were analyzed in this study (Fig. 1), and
the detailed information about genetic instruments for IDPs was
presented in Supplementary Table 2. The phenotypic variance
of IDPs explained by the genetic instruments ranged from 0.5%
for fractional anisotropy (FA) in the right medial lemniscus to
7.50% for intracellular volume fraction in the left inferior fronto-
occipital fasciculus (Supplementary Table 2). The F-statistics for
the genetic instruments of the IDPs ranged from 24 for FA in the
left medial lemniscus to 155 for the volume of the right post-
central gyrus (Supplementary Table 2), suggesting that there was
no instrument bias in this study. This MR analysis had sufficient
power to detect small effect sizes (e.g. OR = 1.2) for the majority of
the IDPs (Supplementary Table 3). The characteristics of GWASs
used for instrument selection and outcome were described in
Supplementary Table 4.
Associations between IDPs and ICH
Figure 2 showed the associations between 363 IDPs and the risk
of ICH in the main analysis, and the detailed results were pre-
sented in Supplementary Table 5. Among these IDPs, we found
that genetically determined high isotropic or free water volume
fraction (ISOVF) in the anterior limb of the left internal capsule
was significantly associated with an increased risk of ICH (OR per
1-SD increase, 4.62; 95% CI, 2.18–9.81; P= 6.63 ×10−5)(Fig. 3). The
characteristic of each genetic variant for ISOVF in the anterior
limb of the left internal capsule was available in Supplementary
Table 6,andSupplementary Fig. 2 illustrated the associations of
each genetic variant for ISOVF in the anterior limb of the left
internal capsule with the risk of ICH.
We also performed a series of sensitivity analyses to validate
the findings in the main analysis (Fig. 3). In the penalized IVW
MR analyses to penalize the SNPs with pleiotropy, genetically
determined high ISOVF in the anterior limb of the left internal
capsule was positively associated with the risk of ICH (OR per 1-SD
increase: 4.19; 95% CI: 2.06–8.52; P= 7.49 ×10−5). In the maximum
likelihood MR analysis, genetically determined high ISOVF in the
anterior limb of the left internal capsule was significantly related
to an increased risk of ICH (OR per 1-SD increase: 4.69; 95%
CI: 2.15–10.19; P= 9.79 ×10−5). In the MR-PRESSO analyses effec-
tive in identifying pleiotropic outliers, the association between
genetically determined high ISOVF in the anterior limb of the
left internal capsule and the risk of ICH (OR per 1-SD increase:
4.19; 95% CI: 1.95–9.02; P= 2.46 ×10−4) remained significant. The
MR-RAPS analyses with robustness to the key MR assumptions
showed a significantly positive association between genetically
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Liu et al. |5
Fig. 3. Association between ISOVF in the anterior limb of the left internal capsule and intracerebral hemorrhage in different MR models. (A) Association
between ISOVF in the anterior limb of the left internal capsule and intracerebral hemorrhage in forward MR analyses. Odds ratios (ORs) and confidence
intervals (CIs) of intracerebral hemorrhage are revealed per 1-SD change in mean ISOVF in the anterior limb of the left internal capsule. The pattern
diagram indicates internal capsule. (B) Association between ISOVF in the anterior limb of the left internal capsule and intracerebral hemorrhage in
reverse MR analyses. Estimates and confidence intervals (CIs) reveal change in mean ISOVF in the anterior limb of the left internal capsule associated
with intracerebral hemorrhage. The pattern diagram indicates internal capsule. Abbreviations: IDP, imaging-derived phenotype; ISOVF, isotropicorfree
water volume fraction; IVW, inverse-variance weighted; MR, Mendelian randomization; MR-RAPS, MR robust adjusted profile score; MR-PRESSO, MR
pleiotropy residual sum and outlier; SNP, single nucleotide polymorphism.
determined high ISOVF in the anterior limb of the left internal
capsule and the risk of ICH (OR per 1-SD increase: 4.25; 95%
CI: 2.00–9.04; P= 1.70 ×10−4). Leave-one-out analyses indicated
that no individual SNP substantially drove these associations
(Supplementary Fig. 2), and the intercept terms of the MR-Egger
regression showed no potential pleiotropy for these associations
(all P>0.05) (Supplementary Table 7).
Reverse MR analyses
Reverse MR analyses were performed to evaluate the potential
causal effect of ICH on ISOVF in the anterior limb of the left
internal capsule. The IVW MR analysis suggested that genetically
determined ICH was not associated with ISOVF in the anterior
limb of the left internal capsule (beta, 0.010; 95% CI, −0.010–0.030;
P= 0.33) (Fig. 3). The characteristic of each genetic variant for ICH
was available in Supplementary Table 8 and Supplementary Fig. 3
illustrated the associations of each genetic variant for ICH with
ISOVF in the anterior limb of the left internal capsule.
The penalized IVW MR analysis (beta: 0.001; 95% CI: −0.020–
0.020; P= 0.91), the maximum likelihood MR analysis (beta:
0.010; 95% CI: −0.010–0.030; P= 0.32), the MR-PRESSO analysis
(beta: −0.002; 95% CI: −0.004–0.001; P= 0.88), and the MR-
RAPS analysis (beta: −0.002; 95% CI: −0.030–0.020; P= 0.84) also
indicated that genetically determined ICH was not associated
with ISOVF in the anterior limb of the left internal capsule (Fig. 3).
Furthermore, leave-one-out analyses indicated that no individual
SNP substantially drove these associations (Supplementary
Fig. 3), and the intercept terms of the MR-Egger regression
showed no potential pleiotropy for these associations (all P>0.05)
(Supplementary Table 7).
Moreover, our findings based on a two-sample MR framework
were hardly influenced by the bias from sample overlap (bias
<0.2, regardless of overlap proportion) (Supplementary Table 9).
Discussion
To our knowledge, this is the first MR study to assess the potential
causal relationship between IDPs and ICH. Among the 363 IDPs,
genetically determined ISOVF in the anterior limb of the left
internal capsule was identified to be positively associated with the
risk of ICH, while no significant association was observed between
genetically predicted ICH and ISOVF in the anterior limb of the left
internal capsule. Sensitivity analyses with different MR models
robustly confirmed these associations. Our findings suggested
that ISOVF in the anterior limb of the left internal capsule might
be a valuable imaging predictor for incident ICH.
Internal capsule is a compact white matter bundle processing
sensory, motor, and visual information (Li et al. 2021). Pathological
changes in the internal capsule have been known to result in
severe sensorimotor deficits in human ICH (Hijioka et al. 2016).
In a prospective cohort study of ICH patients, the internal capsule
was one of the most common predisposing sites for ICH and this
site of bleeding had an influence on the mortality (Smajlovi´
cetal.
2008). Another population-based study also indicated that hem-
orrhagic location on computed tomography scans had prognostic
value for patients with ICH (Eslami et al. 2019). Moreover, it has
been reported that expansion of hematoma into the internal cap-
sule was a critical determinant of the ICH mortality rate and the
severity of neurological dysfunctions after ICH (Matsushita et al.
2013). These findings suggested that the imaging characteristics
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6|Cerebral Cortex, 2024
of internal capsule had the potential to indicate the development
of ICH.
Our study extended this information to the occurrence of ICH,
and demonstrated the important role of internal capsule in the
prediction of ICH. As a fiber tract located between the caudate and
lentiform nuclei (Zhou et al. 2003), the anterior limb of the internal
capsule is responsible for the connection of the thalamic nuclei,
prefrontal cortex, and cingulate gyrus (Thau et al. 2022). ISOVF is
a neurite orientation dispersion and density imaging metric that
can functionally complement diffusion tensor imaging metrics
to detect microstructural changes in white matter (Bagdasarian
et al. 2021). In the present study, we found that high ISOVF in the
anterior limb of the left internal capsule was associated with an
increased risk of ICH, and no reverse association was observed
for them. This is a strictly one-sided relationship that does not
apply to the ISOVF in the anterior limb of the right internal
capsule. Axer et al. (1999) analyzed slices of eight human brain
hemispheres by confocal laser and polarized light microscopy,
and found that the anterior limb of the left internal capsule
had more and smaller fiber tracts than the anterior limb of the
right internal capsule. Similarly, Peled et al. (1998) investigated the
fiber orientation in the internal capsule of 24 healthy volunteers
using MRI and observed that the left internal capsule was more
likely to have fibers crossing or fanning out from the direction of
the main tract than the right internal capsule. Therefore, more
fiber bundles on the anterior limb of the left internal capsule
may be an underlying explanation for this strictly one-sided
relationship. Nevertheless, further studies are warranted to verify
our findings and explore exact mechanisms. Our findings sug-
gested that ISOVF in the anterior limb of the left internal capsule
might be a potential imaging biomarker for the risk prediction
of ICH.
The mechanisms underlying the association between ISOVF in
the anterior limb of the left internal capsule and ICH were not
well defined, although several biological mechanisms have been
suggested. As a predilection site of ICH, basal ganglia contains
plenty of bundles of white matter fiber, while the cerebrospinal
tract in the internal capsule is the main component of these
fiber bundles. Such nerve fibers are vulnerable to the direct
injury due to hematoma compression and pave the way to the
secondary neurological deterioration (Jiang et al. 2019). MRI-based
extracellular free water modeling was known to be sensitive
to neuroinflammation, so ISOVF could be deemed as a brain-
imaging marker of neuroinflammatory response (Andica et al.
2022). It had been reported that the internal capsule was a com-
pact white matter bundle (Li et al. 2021) and ISOVF was effective in
detecting microstructural changes in white matter (Bagdasarian
et al. 2021). Therefore, white matter lesions might be a biologi-
cal rationale underlying the identified association. White matter
hyperintensity is a well-known imaging marker of small vessel
disease damage (Wilson et al. 2014). Several studies had revealed
a relationship between leukoaraiosis and ICH (Smith et al. 2002;
Smith et al. 2004;Neumann-Haefelin et al. 2006;Palumbo et al.
2007). In a prospective cohort study, white matter lesions were
reported as radiological indicators of underlying vasculopathy for
ICH patients without antithrombotic therapy (Smith et al. 2004).
In addition, the amount of hemorrhages and white matter lesions
were both found to be able to indicate the likelihood of a future
vascular rupture (Smith et al. 2004). Collectively, white matter
lesions might be helpful to interpret the identified association.
Previous experimental evidence has supported the etiologic roles
of edema and the mass effect of the hematoma in hemorrhagic
brain damage in internal capsule lesioned model rats, indicating
that accumulated extracellular free water and increased ISOVF in
the internal capsule were potential contributors to the patholo-
gies of ICH (Masuda et al. 2007).
Our study has important clinical and public health implica-
tions in better understanding the etiologic associations between
IDPs and ICH, and further providing new insights in the man-
agement of ICH. Based on our findings, measuring ISOVF in the
anterior limb of the left internal capsule may provide useful
information to identify and monitor high-risk individuals for
ICH, and those with high ISOVF in the anterior limb of the left
internal capsule should receive early intervention with optimal
adjunctive medical therapy to reduce the risk of ICH. Further
studies are needed to confirm our findings and assess the cost-
effectiveness of detecting ISOVF in the anterior limb of the left
internal capsule in the prevention of ICH. In addition, our findings
are preliminary hints and worth further exploring the potential
mechanisms. It is of clinical interest to assess the predictive value
of ISOVF in the anterior limb of the left internal capsule and ICH
in a clinical setting before making assumptions about preventive
strategies.
The present study has several strengths. Firstly, this is the first
MR study to evaluate the potential causal associations between
363 IDPs and ICH using the genomics and neuroimaging data.
Secondly, this study was based on large-scale GWASs and multiple
analytical models, which enabled us to make a reliable causal
inference with a high statistical power. Some limitations should
also be discussed here. Firstly, it is difficult to completely exclude
biases from invalid instruments and pleiotropy in the MR study.
However, in the present study, sensitivity analyses with different
MR models yielded similar results as the main analyses, and the
MR-PRESSO method and the MR-Egger regression suggested no
pleiotropy for the identified association. Therefore, the influence
of invalid instrument and pleiotropy on our findings was min-
imal. Secondly, participant overlap between the exposure and
outcome GWASs may introduce weak instrument bias. Further MR
studies based on the independent cohorts without overlapping
participants are needed to deeper investigate the relationship
between IDPs and ICH. Thirdly, MR estimate represents the risk-
modifying effect of the lifetime exposure to a certain factor,
so the results should not be directly interpreted as the associ-
ations of short-term alterations in imaging features with ICH.
Finally, all the participants included in our study were of European
descent, which lowered the population stratification bias but lim-
ited the generalizability of the findings. Further studies conducted
among non-European populations are needed to confirm our
findings.
Conclusion
In conclusion, in this two-sample MR study, we identified ISOVF
in the anterior limb of the left internal capsule as the potential
causal mediator of ICH. Further studies are needed validate our
findings and determine the potential mechanisms.
Acknowledgments
We thank the investigators of UK Biobank, International Stroke
Genetics consortium, and the European-descent GWASs of brain
imaging-derived phenotypes and intracerebral hemorrhage for
making their results publicly available. Full acknowledgement
and funding statements for each of these resources are available
via the relevant cited reports.
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Liu et al. |7
Author contributions
Yi Liu (Conceptualization, Data curation, Formal analysis, Fund-
ing acquisition, Writing—original draft), Yiming Jia (Conceptu-
alization, Data curation, Formal analysis, Funding acquisition,
Writing—original draft), Hongyan Sun (Conceptualization, Data
curation), Lulu Sun (Conceptualization, Data curation, Formal
analysis), Yinan Wang (Data curation, Formal analysis, Investiga-
tion), Qingyun Xu (Data curation, Formal analysis), Yu He (Formal
analysis, Resources), Xinyue Chang (Data curation, Investigation),
Daoxia Guo (Investigation, Methodology), Mengyao Shi (Inves-
tigation, Methodology), Guo-Chong Chen (Funding acquisition,
Investigation, Methodology), Jin Zheng (Data curation, Funding
acquisition, Investigation, Resources), and Zhengbao Zhu (Con-
ceptualization, Data curation, Formal analysis, Funding acquisi-
tion, Investigation, Methodology).
Supplementary material
Supplementary material is available at Cerebral Cortex online.
Funding
National Natural Science Foundation of China (grant: 82103917),
the Suzhou Science and Technology Project (grant: SKY2023132),
and the Natural Science Foundation of Jiangsu Province (grant:
BK20210716).
Conflict of interest statement: The authors report no conflict of
interest.
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