METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: Lead Investigators from countries formally involved in the EAS FHSC by mid-May 2018 were invited to provide a brief report on FH status in their countries, including available information, programmes, initiatives, and management.
RESULTS: 63 countries provided reports. Data on FH prevalence are lacking in most countries. Where available, data tend to align with recent estimates, suggesting a higher frequency than that traditionally considered. Low rates of FH detection are reported across all regions. National registries and education programmes to improve FH awareness/knowledge are a recognised priority, but funding is often lacking. In most countries, diagnosis primarily relies on the Dutch Lipid Clinics Network criteria. Although available in many countries, genetic testing is not widely implemented (frequent cost issues). There are only a few national official government programmes for FH. Under-treatment is an issue. FH therapy is not universally reimbursed. PCSK9-inhibitors are available in ∼2/3 countries. Lipoprotein-apheresis is offered in ∼60% countries, although access is limited.
CONCLUSIONS: FH is a recognised public health concern. Management varies widely across countries, with overall suboptimal identification and under-treatment. Efforts and initiatives to improve FH knowledge and management are underway, including development of national registries, but support, particularly from health authorities, and better funding are greatly needed.
OBJECTIVES: We sought to define the clinical features that distinguish DOCK8 deficiency from other forms of HIES and CIDs, study the mutational spectrum of DOCK8 deficiency, and report on the frequency of specific clinical findings.
METHODS: Eighty-two patients from 60 families with CID and the phenotype of AR-HIES with (64 patients) and without (18 patients) DOCK8 mutations were studied. Support vector machines were used to compare clinical data from 35 patients with DOCK8 deficiency with those from 10 patients with AR-HIES without a DOCK8 mutation and 64 patients with signal transducer and activator of transcription 3 (STAT3) mutations.
RESULTS: DOCK8-deficient patients had median IgE levels of 5201 IU, high eosinophil levels of usually at least 800/μL (92% of patients), and low IgM levels (62%). About 20% of patients were lymphopenic, mainly because of low CD4(+) and CD8(+) T-cell counts. Fewer than half of the patients tested produced normal specific antibody responses to recall antigens. Bacterial (84%), viral (78%), and fungal (70%) infections were frequently observed. Skin abscesses (60%) and allergies (73%) were common clinical problems. In contrast to STAT3 deficiency, there were few pneumatoceles, bone fractures, and teething problems. Mortality was high (34%). A combination of 5 clinical features was helpful in distinguishing patients with DOCK8 mutations from those with STAT3 mutations.
CONCLUSIONS: DOCK8 deficiency is likely in patients with severe viral infections, allergies, and/or low IgM levels who have a diagnosis of HIES plus hypereosinophilia and upper respiratory tract infections in the absence of parenchymal lung abnormalities, retained primary teeth, and minimal trauma fractures.
METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.
CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.