Abstract 148: Whole Exome Sequence Analysis of Cerebral White Matter Hyperintensities on MRI

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Myriam Fornage ◽  
Joshua C Bis ◽  
Vincent Chouraki ◽  
Li An Lin ◽  
Anita DeStefano ◽  
...  

White matter hyperintensities (WMH) detected on MRI are commonly identified abnormalities in the adult brain, and are associated with a greater risk of stroke, dementia, and death. Genetic factors play a significant role in WMH etiology, yet, common genetic variants identified by GWAS explain little of the variance in WMH burden. Rare variants with larger effect on the phenotype may be identified from sequence analysis of the protein-coding region of the genome (exome). We sequenced the protein-coding regions of 16,541 genes in 2510 individuals of European or African ancestry from three NHLBI cohorts, and investigated whether putatively functional exomic variants were associated with WMH burden, either individually or in aggregate within a gene. Within each cohort, we used the SeqMeta R package to compute race-specific score statistics for each variant and genotypic covariance matrices within predefined gene regions. These were then combined by meta-analysis to generate single-variant and gene-based tests of association. Only missense, nonsense, and splice variants were included in the analyses. Analyses of 12,790 single variants with minor allele frequency (MAF)≥1% did not identify statistically significant associations based on a Bonferroni-corrected significance threshold. The most significantly associated variant was a common missense variant in the ELL gene (p=2.6x10-5). In the sequence kernel association test (SKAT), which included variants with MAF<5%, five genes were significantly associated with WMH burden (p<0.05/16,541). These have known function in phospholipid binding, transport, and signaling (3 genes); Abeta metabolism (1 gene); and have been previously implicated in microvascular complications of diabetes, hypertension, and obesity. Among 35 candidate genes mapping to five previously reported WMH GWAS loci, association of MRPL38 (chr17q25) was nominally significant by SKAT (p=0.011). This gene contained two missense variants (MAF~1%) also nominally significantly associated with WMH (p=0.040 and 0.017). This study suggests that rare and low frequency variants significantly influence WMH burden and that genes involved in cardiovascular health and disease play a role in WMH etiology.

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Kristian L. Funck ◽  
Esben Laugesen ◽  
Pernille Høyem ◽  
Brian Stausbøl-Grøn ◽  
Won Y. Kim ◽  
...  

Abstract Background Stroke is a serious complication in patients with type 2 diabetes (T2DM). Arterial stiffness may improve stroke prediction. We investigated the association between carotid-femoral pulse wave velocity [PWV] and the progression of cerebral white matter hyperintensities (WMH), a marker of stroke risk, in patients with T2DM and controls. Methods In a 5-year cohort study, data from 45 patients and 59 non-diabetic controls were available for analysis. At baseline, participants had a mean (± SD) age of 59  ±  10 years and patients had a median (range) diabetes duration of 1.8 (0.8–3.2) years. PWV was obtained by tonometry and WMH volume by an automated segmentation algorithm based on cerebral T2-FLAIR and T1 MRI (corrected by intracranial volume, cWMH). High PWV was defined above 8.94 m/s (corresponding to the reference of high PWV above 10 m/s using the standardized path length method). Results Patients with T2DM had a higher PWV than controls (8.8  ±  2.2 vs. 7.9  ±  1.4 m/s, p  <  0.01). WMH progression were similar in the two groups (p  =  0.5). One m/s increase in baseline PWV was associated with a 16% [95% CI 1–32%], p  <  0.05) increase in cWMH volume at 5 years follow-up after adjustment for age, sex, diabetes, pulse pressure and smoking. High PWV was associated with cWMH progression in the combined cohort (p  <  0.05). We found no interaction between diabetes and PWV on cWMH progression. Conclusions PWV is associated with cWMH progression in patients with type 2 diabetes and non-diabetic controls. Our results indicate that arterial stiffness may be involved early in the pathophysiology leading to cerebrovascular diseases.


NeuroImage ◽  
2009 ◽  
Vol 47 (1) ◽  
pp. 199-203 ◽  
Author(s):  
Richard Beare ◽  
Velandai Srikanth ◽  
Jian Chen ◽  
Thanh G. Phan ◽  
Jennifer Stapleton ◽  
...  

2018 ◽  
Vol 14 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Alice Trentalange ◽  
Adolfo Prochet ◽  
Daniele Imperiale ◽  
Jessica Cusato ◽  
Mariacristina Tettoni ◽  
...  

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Yan Chen ◽  
Renyuan Liu ◽  
Shuwei Qiu ◽  
Yun Xu

Introduction: Cerebral White matter hyperintensities(WMH) are frequent findings on MRI scan. They are well known to correlate with vascular cognitive impairment(VCI). However, controversies still remain about the relationship between WMH locations and cognitive function across studies. Hypothesis: Periventricular WMHs(PWH) rather than deep WMHs(DWH) are associated with cognitive decline in VCI. Methods: Fifty-nine subjects with WMHs on MRI were divided into three groups, normal control(NC), mild cognitive impairment(MCI) and vascular dementia(VaD), according to clinical manifestation and neuropsychological performance. WMH volumes were evaluated by Fazekas rating scale and segmental volumetric. Correlations between cognitive performance and WMH volumes were determined in virtue of Spearman correlation analysis. Receiver operator characteristic (ROC) curves were generated to define the classification cut-off value of WMH volumes for distinguishing VCI versus normal controls. Multiple linear regression analysis was used to predict cognitive performance with WMH volumes and locations after adjusting for sex ,age and education level. Results: Cognitive capacities were gradually declined from NC through MCI to VaD patients while WMH volumes and Fazekas scores altered oppositely. Both PWH and DWH volumes and Fazekas scores were correlated with cognitive performance, and moreover, WMH volumes were correlated with Fazekas scores. ROC analysis showed a cut-off value of PWH rather than DWH to distinguish VCI from NC(AUC=0.745 and 0.635, p =0.001 and 0.076, respectively). Linear regression analysis demonstrated that only PWH volumes were associated with cognitive performance( p < 0.001). Conclusion: Our study demonstrate that PWHs are independent predictors for vascular contribution in white matter lesions and suggest clinicians that PWH should be emphasized on evaluating vascular cognitive impairment related with white matter load.


2018 ◽  
Vol 38 (12) ◽  
pp. 2843-2853 ◽  
Author(s):  
Junichiro Hashimoto ◽  
Berend E. Westerhof ◽  
Sadayoshi Ito

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