Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10−8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10−8). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies

Forest plot of the meta-analysis of rs7077361 in <i>ITGA8</i>.

<p>Study-specific allelic odds ratios (ORs, black squares) and 95% confidence intervals (CIs, lines) were calculated for each included dataset. The summary OR and CI was calculated using the DerSimonian Laird random-effects model (grey diamond) <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002548#pgen.1002548-DerSimonian1" target="_blank">[31]</a>. C = Caucasian ancestry.</p

Flowchart of literature search, data extraction, and analysis strategies applied for PDGene.

<p>Flowchart of literature search, data extraction, and analysis strategies applied for PDGene.</p

Manhattan plot of all meta-analysis results performed in PDGene.

<p>This summary combines association results from 7,123,986 random-effects meta-analyses based on the March 31^st 2011 datafreeze of the PDGene database. Results are plotted as −log₁₀<i>P</i>-values (y-axis) against physical chromosomal location (x-axis). Black and grey dots indicate results originating exclusively from the three fully publicly available GWAS datasets <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002548#pgen.1002548-Maraganore2" target="_blank">[10]</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002548#pgen.1002548-Pankratz1" target="_blank">[12]</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002548#pgen.1002548-SimnSnchez1" target="_blank">[13]</a> (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002548#s4" target="_blank">Methods</a>), while green dots are based on a combination of smaller scale studies, supplemented by GWAS datasets (where applicable). Gene annotations are provided for genes highlighted in the main text.</p

Overview of genome-wide association studies (GWAS) published in PD until March 31, 2011.

<p>The overview is based on content on the PDGene website (<a href="http://www.pdgene.org" target="_blank">http://www.pdgene.org</a>; current on March 31^st, 2011). Studies are listed in order of publication date. ‘# PD GWAS’ and ‘# CTRL GWAS’ refers to sample sizes used in the initial GWAS datasets, whereas ‘Follow-up’ refers to the total number of replication samples where applicable. ‘Featured genes’ are those genes/loci that were declared as ‘associated’ in the original publication; note that criteria for declaring association varies across studies. Genetic loci in bold font denote genes showing genome-wide significant results (<i>P</i><5×10⁻⁸) in the PDGene meta-analyses.</p

Management and outcomes in critically ill nonagenarian versus octogenarian patients

Background: Intensive care unit (ICU) patients age 90 years or older represent a growing subgroup and place a huge financial burden on health care resources despite the benefit being unclear. This leads to ethical problems. The present investigation assessed the differences in outcome between nonagenarian and octogenarian ICU patients. Methods: We included 7900 acutely admitted older critically ill patients from two large, multinational studies. The primary outcome was 30-day-mortality, and the secondary outcome was ICU-mortality. Baseline characteristics consisted of frailty assessed by the Clinical Frailty Scale (CFS), ICU-management, and outcomes were compared between octogenarian (80-89.9 years) and nonagenarian (>= 90 years) patients. We used multilevel logistic regression to evaluate differences between octogenarians and nonagenarians. Results: The nonagenarians were 10% of the entire cohort. They experienced a higher percentage of frailty (58% vs 42%; p < 0.001), but lower SOFA scores at admission (6 +/- 5 vs. 7 +/- 6; p < 0.001). ICU-management strategies were different. Octogenarians required higher rates of organ support and nonagenarians received higher rates of life-sustaining treatment limitations (40% vs. 33%; p < 0.001). ICU mortality was comparable (27% vs. 27%; p = 0.973) but a higher 30-day-mortality (45% vs. 40%; p = 0.029) was seen in the nonagenarians. After multivariable adjustment nonagenarians had no significantly increased risk for 30-day-mortality (aOR 1.25 (95% CI 0.90-1.74; p = 0.19)). Conclusion: After adjustment for confounders, nonagenarians demonstrated no higher 30-day mortality than octogenarian patients. In this study, being age 90 years or more is no particular risk factor for an adverse outcome. This should be considered- together with illness severity and pre-existing functional capacity - to effectively guide triage decisions

Analysis of Outcomes in Ischemic vs Nonischemic Cardiomyopathy in Patients With Atrial Fibrillation A Report From the GARFIELD-AF Registry

IMPORTANCE Congestive heart failure (CHF) is commonly associated with nonvalvular atrial fibrillation (AF), and their combination may affect treatment strategies and outcomes