scholarly journals Tumor necrosis factor-α and -β genetic polymorphisms as a risk factor in Saudi patients with schizophrenia

2017 ◽  
Vol Volume 13 ◽  
pp. 1081-1088 ◽  
Author(s):  
Saeed Kadasah ◽  
Misbahul Arfin ◽  
Sadaf Rizvi ◽  
Mohammed Al-Asmari ◽  
Abdulrahman Al-Asmari
Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2604-2604
Author(s):  
Jaime Fernandez De Velasco ◽  
Pilar Llamas ◽  
Raquel De Ona ◽  
Ana Belen Santos ◽  
Elena Meseguer ◽  
...  

Abstract Introduction: Cerebrovascular disease (CVD) is a multifactorial disease caused by the interaction of genetic and environmental factors. The atherosclerotic plaque, the pathological hallmark of CVD, is an inflammatory process, where pro-inflammatory cytokines, such as tumor necrosis factor alpha (TNFα). TNFα secretion shows a high degree of interindividual variability, which is at least partly genetically determined. We have analysed the prevalence of −238 G/A and −308 G/A polymorphisms in the regulatory region of the TNFα gene promoter in CVD. Patients and methods: Genotypic analyses were performed on 308 consecutive unrelated patients diagnosed with ischemic CVD, 147 women and 161 men, mean age 70±0.8 years, who were diagnosed according to the Trial of Org 10172 in Acute Stroke Treatment. All included cases were age and sex matched to a control from the same geographic area who had no history of vascular disease. Patients and controls completed a questionnaires including blood pressure, diabetes status, total serum cholesterol level and smoking history. The TNFα variants were detected by PCR using primers containing a single base-pair mismatch to introduce a restriction site into the wild-type nucleotide sequences after amplification. PCR products were digested with NcoI and MspI to detect −308 and −238 variants, respectively. The strength of the association of the polymorphisms with the occurrence of CVD was estimated by calculation of the OR and its 95%CI by exact method. P values less than 0.05 were considered significant. Logistic regression analysis was applied to estimate the risk in a multivariable predictive model with dependent variable (case/control) and all independent variables significant in the bivariate analysis. SPSS 9.0 was used for the statistical analysis. Results: Genotype analysis showed a significant higher prevalence of the G/A and A/A genotypes of −238 G/A TNFα in patients (p< 0.01;OR= 2.16;95%CI= 1.40–3.34). The prevalence of the A allele was also significantly increased in the group of CVD patients than in the controls (13.6% vs 7.0%; p<0.01;OR=2.10;95%CI=1.40–3.17). By contrast, the distribution of −308 genotypes for patients did not differ from their control group (84.9% vs 81.1% and 14.4% vs 16.9%, respectively; p=0.21;OR=0.76;95%CI=0.48–1.20) or in the A allele frequencies (7.9% vs 10.4%; p=0.12;OR=0.73;95%CI=0.48–1.11). When analysis was performed for the two more frequently subtypes of CVD, atherotrombotic subtype showed an higher prevalence of the A allele of -238 G/A polymorphism, as compared with the undetermined etiology (16.5% vs 11.2%;p=0.06;OR=1.86;95%CI= 0.91–3.82). By contrast, the variant −308 G/A did not differ from the different subtypes. Logistic regression analysis showed a independent association of A haplotype of −238 G/A TNFα with CVD. Also hypertension, diabetes mellitus and current smoking status were statistically associated with CVD. Conclusions: Our findings suggest that the A allele of −238 G/A TNFα promoter polymorphism is a genetic risk factor for ischemic CVD in Spanish population. Data on the distribution of genotypes and corresponding allelic frequencies of −238 G/A and −308 G/A TNFα polymorphisms in CVD are scant. The present study is the first to analyses these variants in CVD patients from this geographic area.


2018 ◽  
Vol 59 (7) ◽  
pp. 653-667 ◽  
Author(s):  
Zareen Sultana ◽  
Biswabandhu Bankura ◽  
Arup Kumar Pattanayak ◽  
Debmalya Sengupta ◽  
Mainak Sengupta ◽  
...  

2015 ◽  
Vol 40 (12) ◽  
pp. 1218-1224 ◽  
Author(s):  
Sushil K. Dubey ◽  
James F. Hejtmancik ◽  
Subbaiah R. Krishnadas ◽  
Rajendrababu Sharmila ◽  
Aravind Haripriya ◽  
...  

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