The influence of OLR1 and PCSK9 gene polymorphisms on ischemic stroke: Evidence from a meta-analysis

It has been reported that both OLR1 and PCSK9 genes are related to various vascular diseases such as atherosclerosis, cardiovascular disease, peripheral artery disease and stroke, in particular ischemic stroke. The prevalence of PCSK9 rs505151 and OLR1 rs11053646 variants in ischemic stroke were 0.005 and 0.116, respectively. However, to date, association between OLR1 rs11053646 and PCSK9 rs505151 polymorphisms and the risk of ischemic stroke remains unclear and inconclusive. Therefore, this first meta-analysis was carried out to clarify the presumed influence of genetic polymorphisms on ischemic stroke, by analyzing the complete coverage of all relevant studies. All eligible case-control and cohort studies that met the search term were retrieved in multiple scientific databases. Data of interest such as demographic data and genotyping methods were extracted from each study, and the meta-analysis was performed using RevMan 5.3 and Metafor R 3.2.1. The pooled odd ratios (ORs) and 95% confidence intervals (CIs) were calculated using both fixed- and random-effect models. A total of seven case-control studies encompassing 1897 ischemic stroke cases and 2119 healthy controls were critically evaluated. Pooled results from the genetic models indicated that OLR1 rs11053646 dominant (OR=1.33. 95%CI:1.11-1.58) and co-dominant models (OR=1.24, 95%CI:1.02-1.51) were significantly associated with ischemic stroke. For PCSK9 rs505151 polymorphism, the OR of co-dominant model (OR=1.36, 95%CI:1.01-1.58) was found to be higher among ischemic stroke patients. In conclusion, the current meta-analysis highlighted that variant allele of OLR1 rs11053646 G>C and PCSK9 rs505151 A>G may contribute to the susceptibility risk of ischemic stroke

Association of NOTCH3 gene polymorphisms with ischemic stroke and its subtypes: A meta-analysis

Background and objectives: NOTCH3 gene variations play a significant role in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). However, the role of NOTCH3 gene polymorphisms in the risk of ischemic stroke, and its subtypes such as atherothrombotic or lacunar strokes, remains unclear. Aims: Hence, we carried out a meta-analysis to examine whether the NOTCH3 rs1043994, rs1044009 and rs3815188 polymorphisms are associated with ischemic stroke and its major subtypes. Materials and Methods: All relevant studies were systematically screened and meta-analyzed using Review Manager (Revman) version 5.3. The strength of the association between NOTCH3 polymorphisms and ischemic stroke risk and its subtypes were measured as odds ratios and 95% confidence intervals, under different genetic models. Results: A total of ten studies were identified, five of which considered NOTCH3 rs1043994 (2077 cases/2147 controls), five of which considered NOTCH3 rs1044009 (2315 cases/3053 controls), and nine of which considered NOTCH3 rs3815188 (2819 cases/2769 controls). These studies were meta-analyzed for their association with ischemic stroke risk. Four studies (874 cases/2002 controls) of the NOTCH3 rs3815188 polymorphism and three studies of the NOTCH3 rs1043994 (643 cases/1552 controls) polymorphism were meta-analyzed for lacunar stroke risk. Three studies (1013 cases/1972 controls) of the NOTCH3 rs3815188 polymorphism were meta-analyzed for atherothrombotic stroke risk. The meta-analysis results showed a lack of association between all of the studied polymorphisms and the risk of ischemic stroke and its major subtypes (i.e., atherothrombotic and lacunar). Conclusions: NOTCH3 polymorphisms are not significantly associated with the risk of ischemic stroke and its subtypes (p < 0.05).</p

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

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.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.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.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

The impact of APOA5 , APOB , APOC3 and ABCA1 gene polymorphisms on ischemic stroke: Evidence from a meta-analysis

<b>BACKGROUND AND AIMS</b>\ud \ud Genetic studies have been reported on the association between APOA5, APOB, APOC3 and ABCA1 gene polymorphisms and ischemic stroke, but results remain controversial. Hence, this meta-analysis aimed to infer the causal relationships of APOA5 (rs662799, rs3135506), APOB (rs693, rs1042031, rs1801701), APOC3 (rs4520, rs5128, rs2854116, rs2854117) and ABCA1 rs2230806 with ischemic stroke risk.\ud \ud <b>METHODS</b>\ud \ud A systematic review was performed for all the articles retrieved from multiple databases, up until March 2017. Data were extracted from all eligible studies, and meta-analysis was carried out using RevMan 5.3 and R package 3.2.1. The strength of association between each studied polymorphism and ischemic stroke risk was measured as odds ratios (ORs) and 95% confidence intervals (CIs), under fixed- and random-effect models.\ud \ud <b>RESULTS</b>\ud \ud A total of 79 studies reporting on the association between the studied polymorphisms and ischemic stroke risk were identified. The pooled data indicated that all genetic models of APOA5 rs662799 (ORs = 1.23-1.43), allelic and over-dominant models of APOA5 rs3135506 (ORs = 1.77-1.97), APOB rs1801701 (ORs = 1.72-2.13) and APOB rs1042031 (ORs = 1.66-1.88) as well as dominant model of ABCA1 rs2230806 (OR = 1.31) were significantly associated with higher risk of ischemic stroke. However, no significant associations were observed between ischemic stroke and the other five polymorphisms, namely ApoB (rs693) and APOC3 (rs4520, rs5128, rs2854116 and rs2854117), under any genetic model.\ud \ud <b>CONCLUSIONS:</b>\ud \ud The present meta-analysis confirmed a significant association of APOA5 rs662799 CC, APOA5 rs3135506 CG, APOB rs1801701 GA, APOB rs1042031 GA and ABCA1 rs2230806 GG with increased risk of ischemic stroke

Significant factors associated with severe dengue (using multiple logistic regressions).

<p>Significant factors associated with severe dengue (using multiple logistic regressions).</p

Percentage of warning signs by dengue severity.

<p>Percentage of warning signs by dengue severity.</p

Prevalence of drug-related problems and complementary and alternative medicine use in Malaysia:A systematic review and meta-analysis of 37,249 older adults

Drug-related problems (DRPs) in the elderly include polypharmacy, potentially inappropriate medications, nonadherence, and drug-related falls. In this systematic review and meta-analysis, the prevalence of DRPs and complementary and alternative medicine (CAM) use among the Malaysian elderly was estimated. PubMed, Scopus, Web of Science, and Google Scholar databases were searched to identify studies published since their inception up to 24 August 2020. A random-effects model was used to generate the pooled prevalence of DRPs along with its corresponding 95% confidence interval (CI). The heterogeneity of the results was estimated using the I2 statistics, and Cochran’s Q test and sensitivity analyses were performed to confirm the robustness of the results. We identified 526 studies, 23 of which were included in the meta-analysis. (n = 29,342). The pooled prevalence of DRPs among Malaysian elderly was as follows: (1) polypharmacy: 49.5% [95% CI: 20.5–78.6], (2) potentially inappropriate medications: 28.9% [95% CI: 25.4–32.3], (3) nonadherence to medications: 60.6% [95% CI: 50.2–70.9], and (4) medication-related falls 39.3% [95% CI: 0.0–80.8]. Approximately one in two Malaysian elderly used CAM. The prevalence of polypharmacy and potentially inappropriate medications among the Malaysian elderly population was high, calling for measures and evidence-based guidelines to ensure the safe medication use

Decision tree for severe dengue infection.

<p>Decision tree for severe dengue infection.</p

Comparison of decision tree with actual clinical diagnosis.

<p>Comparison of decision tree with actual clinical diagnosis.</p

Demographic profiles of dengue patients.

<p>Demographic profiles of dengue patients.</p