METHODOLOGY: This is a prospective study where patients (n=119) blood was tested for anti-HAVIgG and CYP3A4*18 polymorphism.
RESULTS: The overall anti-HAV seroprevalence was 88.2%. The etiology of CLD was hepatitis B in 96 patients (80.7%) and hepatitis C in 23 patients (19.3%). There was a significant increase in the age of the prevalence of this disease after 30 years of age (p=0.008). CYP3A4*18 polymorphism was detected in 3 (2.5%) of the patients with chronic liver disease. However, there was no significant association between CP3A4*18 mutation and anti-HAV serology.
CONCLUSIONS: Age was the most important factor in determining anti-HAV positivity. It is concluded that CYP3A4*18 genetic polymorphism does not play a main role in influencing the seroprevalence of anti-hepatitis A among chronic viral hepatitis B and C liver disease patients.
OBJECTIVE: This meta-analysis aimed to assess the updated pooled effects of these polymorphisms with DN among Asian populations with type 2 diabetes mellitus.
METHODS: The PubMed electronic database was searched without duration filter until August 2017 and the reference list of eligible studies was screened. The association of each polymorphism with DN was examined using odds ratio and its 95% confidence interval based on dominant, recessive and allele models. Subgroup analyses were conducted based on region, DN definition and DM duration.
RESULTS: In the main analysis, the ACE I/D (all models) and AGTR1 A1166C (dominant model) showed a significant association with DN. The main analysis of the AGT M235T polymorphism did not yield significant findings. There were significant subgroup differences and indication of significantly higher odds for DN in terms of DM duration (≥10 years) for ACE I/D (all models), AGT M235T (recessive and allele models) and AGTR1 A1166C (recessive model). Significant subgroup differences were also observed for DN definition (advanced DN group) and region (South Asia) for AGTR1 A1166C (recessive model).
CONCLUSION: In the Asian populations, ACE I/D and AGTR1 A1166C may contribute to DN susceptibility in patients with T2DM by different genetic models. However, the role of AGT M235T needs to be further evaluated.
METHOD: Ten DPP4 SNPs were genotyped by TaqMan genotyping assays in 314 subjects with T2DM and 235 controls. Of these, 71 metabolic syndrome (MetS) subjects were excluded from subsequent analysis. The odds ratios (ORs) and their 95% confidence interval (CIs) were calculated using multiple logistic regression for the association between the SNPs of DPP4 and T2DM. In addition, the serum levels of sDPP-IV were investigated to evaluate the association of the SNPs of DPP4 with the sDPP-IV levels.
RESULTS: Dominant, recessive, and additive genetic models were employed to test the association of DPP4 polymorphisms with T2DM, after adjusting for age, race, gender and BMI. The rs12617656 was associated with T2DM in Malaysian subjects in the recessive genetic model (OR = 1.98, p = 0.006), dominant model (OR = 1.95, p = 0.008), and additive model (OR = 1.63, p = 0.001). This association was more pronounced among Malaysian Indians, recessive (OR = 3.21, p = 0.019), dominant OR = 3.72, p = 0.003) and additive model (OR = 2.29, p = 0.0009). The additive genetic model showed that DPP4 rs4664443 and rs7633162 polymorphisms were associated with T2DM (OR = 1.53, p = 0.039), and (OR = 1.42, p = 0.020), respectively. In addition, the rs4664443 G>A polymorphism was associated with increased sDPP-IV levels (p = 0.042) in T2DM subjects.
CONCLUSIONS: DPP4 polymorphisms were associated with T2DM in Malaysian subjects, and linked to variations in sDPP-IV levels. In addition, these associations were more pronounced among Malaysian Indian subjects.
METHODS: Seven single-nucleotide polymorphisms (SNPs) in IKZF1, three SNPs in DDC, two SNPs in CDKN2A, two SNPs in CEBPE, and three SNPs in LMO1 were genotyped in 289 Yemeni children (136 cases and 153 controls), using the nanofluidic Dynamic Array (Fluidigm 192.24 Dynamic Array). Logistic regression analyses were used to estimate ALL risk, and the strength of association was expressed as odds ratios with 95% confidence intervals.
RESULTS: We found that the IKZF1 SNP rs10235796 C allele (p = 0.002), the IKZF1 rs6964969 A>G polymorphism (p = 0.048, GG vs. AA), the CDKN2A rs3731246 G>C polymorphism (p = 0.047, GC+CC vs. GG), and the CDKN2A SNP rs3731246 C allele (p = 0.007) were significantly associated with ALL in Yemenis of Arab-Asian descent. In addition, a borderline association was found between IKZF1 rs4132601 T>G variant and ALL risk. No associations were found between the IKZF1 SNPs (rs11978267; rs7789635), DDC SNPs (rs3779084; rs880028; rs7809758), CDKN2A SNP (rs3731217), the CEBPE SNPs (rs2239633; rs12434881) and LMO1 SNPs (rs442264; rs3794012; rs4237770) with ALL in Yemeni children.
CONCLUSION: The IKZF1 SNPs, rs10235796 and rs6964969, and the CDKN2A SNP rs3731246 (previously unreported) could serve as risk markers for ALL susceptibility in Yemeni children.
Methods: In this cross-sectional study, we recruited all 122 preclinical medical students. The validated depression anxiety stress scales-21 (DASS-21) questionnaire was distributed and blood samples were collected from each subject for DNA extraction. Genotyping analysis of the BDNF gene (Val66Met) polymorphism was performed via an optimised polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method.
Results: A total of 105 subjects agreed to participate in this study. Indian students were found to more likely have the Val/Val genotype, whereas Malay students were more likely to have the Met/Met genotype (p = 0.027). Individuals carrying any one of the three BDNF genotypes (Val/Val, Val/Met and Met/Met) differed significantly from each other in terms of their perception of stress (p = 0.010); students carrying the Val/Val genotype (M = 10.6) perceived significantly lower stress than students carrying the Val/Met (M = 14) and Met/Met (M = 15.1) genotypes.
Conclusion: In our study, the Met-allele was associated with higher stress levels. To the best of our knowledge, this is the first study investigating this stress-related gene in medical students. The findings from this study should trigger more investigators to focus on the impact of stress on genetically predisposed medical students.
METHODS: Blood samples from 511 febrile patients were collected and a partial region of the 18 s ribosomal RNA (18 s rRNA) gene was amplified using nested PCR. From the 86 positive blood samples, 13 Plasmodium falciparum and 4 Plasmodium vivax were selected and underwent cloning and, subsequently, sequencing and the sequences were subjected to phylogenetic analysis using the neighbor-joining and maximum parsimony methods.
RESULTS: Malaria was detected by PCR in 86 samples (16.8%). The majority of the single infections were caused by P. falciparum (80.3%), followed by P. vivax (5.8%). Mixed infection rates of P. falciparum + P. vivax and P. falciparum + P. malariae were 11.6% and 2.3%, respectively. All P. falciparum isolates were grouped with the strain 3D7, while P. vivax isolates were grouped with the strain Salvador1. Phylogenetic trees based on 18 s rRNA placed the P. falciparum isolates into three sub-clusters and P. vivax into one cluster. Sequence alignment analysis showed 5-14.8% SNP in the partial sequences of the 18 s rRNA of P. falciparum.
CONCLUSIONS: Although P. falciparum is predominant, P. vivax, P. malariae and mixed infections are more prevalent than has been revealed by microscopy. This overlooked distribution should be considered by malaria control strategy makers. The genetic polymorphisms warrant further investigation.