ABSTRACT
Background and purpose: Despite evidence for the role of genetic factors in stroke, only a small proportion of strokes have been clearly attributed to monogenic factors, due to phenotypic heterogeneity. The goal of this study was to determine whether a significant relationship exists between human galectin-7 gene LGALS7 promoter region polymorphisms and the risk of stroke due to non-traumatic intracerebral hemorrhage (ICH). Methods: This two-stage genetic association study included an initial exploratory stage followed by a discovery stage. During the exploratory stage, transgenic galectin-7 mice or transgenic mice with the scrambled sequence of the hairpin structure -silenced down gene LGALS7-were generated and then expressed differentially expressed proteins and galectin-7-interacting proteins were identified through proteomic analysis. During the discovery stage, a single-nucleotide polymorphism (SNP) genotyping approach was used to determine associations between 2 LGALS7 SNPs and ICH stroke risk for a cohort of 24 patients with stroke of the Chinese Han population and 70 controls. Results: During the exploratory phase, LGALS7 expression was found to be decreased in TGLGALS-DOWN mice as compared to its expression in TGLGALS mice. During the discovery phase, analysis of LGALS7 sequences of 24 non-traumatic ICH cases and 70 controls led to the identification of 2 ICH susceptibility loci: a genomic region on 19q13.2 containing two LGALS7 SNPs, rs567785577 and rs138945880, whereby the A allele of rs567785577 and the T allele of rs138945880 were associated with greater risk of contracting ICH [for T and A vs. C and G, unadjusted odds ratio (OR) = 13.5; 95% CI = 2.249-146.5; p = 0.002]. This is the first study to genotype the galectin-7 promoter in patients with hemorrhagic stroke. Genotype and allele association tests and preliminary analysis of patients with stroke revealed that a single locus may be a genetic risk factor for hemorrhagic stroke. Conclusion: A and T alleles of two novel SNP loci of 19q13.2, rs567785577 and rs138945880, respectively, were evaluated for associations with susceptibility to ICH. Further studies with expanded case numbers that include subjects of other ethnic populations are needed to elucidate mechanisms underlying associations between these SNPs and ICH risk.
ABSTRACT
Background and Purpose: Subarachnoid hemorrhage (SAH) has long been classified into two main forms, aneurysmal SAH (aSAH) and non-aneurysmal SAH (naSAH), but the related risk factors for aSAH and naSAH are heterogeneous. Our objective was to determine the risk factors for SAH of known or unknown origin with respect to diagnostic evaluation in a large patient cohort. We sought to determine whether our classification system can further predict middle long-term stroke and death. Methods: We performed a systematic review and meta-analysis to identify risk factors for each SAH subtype. The discovery phase analyzed 11 risk factors from case studies in the literature. Kruskal-Wallis, Cox regression, logistic regression, and Kaplan-Meier analyses were used to compare the two groups. Results: A total of 14,904 (34.53%) male and 22,801 (52.84%) female patients were eligible for this study. At a median follow-up of 45.6 months, the 5-years overall survival was 97.768% (95% CI: 0.259-0.292) for aSAH patients and 87.904% (95% CI: 1.459-1.643) for naSAH patients. The 10-years survival rate was 93.870% (95% CI: 2.075-3.086) and 78.115% (95% CI: 2.810-3.156), respectively. Multi-risk factor subgroups showed significant intergroup differences. We identified eight risk factors (drugs, trauma, neoplastic, vessels lesion, inflammatory lesion, blood disease, aneurysm, peri-mesencephalic hemorrhage) using logistic regression, which were optimally differentiated among the aSAH [aSAH-S (AUC: 1), a-d-SAH (AUC: 0.9998), aSAH-T (AUC: 0.9199), aSAH-N (AUC: 0.9433), aSAH-V (AUC: 1), aSAH-I (AUC: 0.9954), a-bd-SAH (AUC: 0.9955)] and naSAH [na-pmSAH (AUC: 0.9979), na-ni-ivl-SAH (AUC: 1), na-t-SAH (AUC: 0.9997), na-ne-SAH (AUC: 0.9475), na-d-SAH (AUC: 0.7676)] subgroups. These models were applied in a parallel cohort, showing eight risk factors plus survival rates to predict the prognosis of SAH. Conclusions: The classification of risk factors related to aSAH and naSAH is helpful in the diagnosis and prediction of the prognosis of aSAH and naSAH patients. Further validation is needed in future clinical applications.
