SLC17A3 rs9379800 and Ischemic Stroke Susceptibility at the Northern Region of Malaysia.

OBJECTIVES: The relationships of Paired Like Homeodomain 2 (PITX2), Ninjurin 2 (NINJ2), TWIST-Related Protein 1 (TWIST1), Ras Interacting Protein 1 (Rasip1), Solute Carrier Family 17 Member 3 (SLC17A3), Methylmalonyl Co-A Mutase (MUT) and Fer3 Like BHLH Transcription Factor (FERD3L) polymorphisms and gene expression with ischemic stroke have yet to be determined in Malaysia. Hence, this study aimed to explore the associations of single nucleotide polymorphisms (SNPs) and gene expression with ischemic stroke risk among population who resided at the Northern region of Malaysia. MATERIALS AND METHODS: Study subjects including 216 ischemic stroke patients and 203 healthy controls were recruited upon obtaining ethical clearance. SNP genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism assays. Gene expression levels were quantified by real-time polymerase chain reaction assays. Statistical and genetic analyses were conducted with SPSS version 22.2, PLINK version 1.07 and multifactor dimensionality reduction software. RESULTS: Study subjects with G allele, CG or GG genotypes of SLC17A3 rs9379800 demonstrated increased risk of ischemic stroke with the odds ratios ranging from 1.76-fold to 3.14-fold (p<0.05). When stratified study subjects according to the ethnicity, SLC17A3 rs9379800 G allele and CG genotype contributed to 2.14- and 2.96-fold of ischemic stroke risk among Malay population significantly, in the multivariate analysis (p<0.05). However, no significant associations were observed for PITX2, NINJ2, TWIST1, Rasip1, and MUT polymorphisms with ischemic stroke risk in the multivariate analysis for the pooled cases and controls as well as when stratified them according to the ethnicity. Lower mRNA expression levels of Rasip1, SLC17A3, MUT and FERD3L were observed among cases (p<0.05). After FDR adjustment, the mRNA level of SLC17A3 remained significantly associated with ischemic stroke among Malay population (q=0.034). CONCLUSION: In conclusion, this study suggests that SLC17A3 rs9379800 polymorphism and its gene expression contribute to significant ischemic stroke risk among Malaysian population, particularly the Malay who resided at the Northern Region of the country. Our findings can provide useful information for the future diagnosis, management and treatment of ischemic stroke patients.

Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection.

BACKGROUND: WHO's new classification in 2009: dengue with or without warning signs and severe dengue, has necessitated large numbers of admissions to hospitals of dengue patients which in turn has been imposing a huge economical and physical burden on many hospitals around the globe, particularly South East Asia and Malaysia where the disease has seen a rapid surge in numbers in recent years. Lack of a simple tool to differentiate mild from life threatening infection has led to unnecessary hospitalization of dengue patients. METHODS: We conducted a single-centre, retrospective study involving serologically confirmed dengue fever patients, admitted in a single ward, in Hospital Kuala Lumpur, Malaysia. Data was collected for 4 months from February to May 2014. Socio demography, co-morbidity, days of illness before admission, symptoms, warning signs, vital signs and laboratory result were all recorded. Descriptive statistics was tabulated and simple and multiple logistic regression analysis was done to determine significant risk factors associated with severe dengue. RESULTS: 657 patients with confirmed dengue were analysed, of which 59 (9.0%) had severe dengue. Overall, the commonest warning sign were vomiting (36.1%) and abdominal pain (32.1%). Previous co-morbid, vomiting, diarrhoea, pleural effusion, low systolic blood pressure, high haematocrit, low albumin and high urea were found as significant risk factors for severe dengue using simple logistic regression. However the significant risk factors for severe dengue with multiple logistic regressions were only vomiting, pleural effusion, and low systolic blood pressure. Using those 3 risk factors, we plotted an algorithm for predicting severe dengue. When compared to the classification of severe dengue based on the WHO criteria, the decision tree algorithm had a sensitivity of 0.81, specificity of 0.54, positive predictive value of 0.16 and negative predictive of 0.96. CONCLUSION: The decision tree algorithm proposed in this study showed high sensitivity and NPV in predicting patients with severe dengue that may warrant admission. This tool upon further validation study can be used to help clinicians decide on further managing a patient upon first encounter. It also will have a substantial impact on health resources as low risk patients can be managed as outpatients hence reserving the scarce hospital beds and medical resources for other patients in need.