NHLBI TOPMed: Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA414681 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:NHLBI TOPMed: Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)
| PRJNA414680 | ENA
Project description:Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)
| PRJNA354105 | ENA
Project description:Background: COPD is currently the fourth leading cause of death worldwide and predicted to rank third by 2020. Statins are commonly used lipid lowering agents with documented benefits on cardiovascular morbidity and mortality, and have also been shown to have pleiotropic effects including anti-inflammatory and anti-oxidant activity. Objective: Identify a gene signature associated with statin use in the blood of COPD patients, and identify molecular mechanisms and pathways underpinning this signature that could explain any potential benefits in COPD. Methods: Whole blood gene expression was measured on 168 statin users and 452 non-users from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study. Gene expression was measured using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data and to filter out non-informative probe sets. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and employing a surrogate variable analysis. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results: 18 genes were differentially expressed between statin users and non-users at a false discovery rate of 10%. Top genes included LDLR, ABCA1, ABCG1, MYLIP, SC4MOL, and DHCR24. The 18 genes were significantly enriched in pathways and biological processes related to cholesterol homeostasis and metabolism, and were enriched for transcription factor binding sites for sterol regulatory element binding protein 2 (SREBP-2). The resulting gene signature showed correlation with Huntington disease, Parkinson’s disease and acute myeloid leukemia. Conclusion: Statins gene signature was not enriched in any pathways related to respiratory diseases, beyond the drug’s effect on cholesterol homeostasis. Study subjects were a subset of those with COPD from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (Vestbo et al.), funded by GlaxoSmithKline (GSK Study No. SCO104960, NCT00292552). ECLIPSE is a non-interventional, observational, multicentre, three-year study in people with COPD. Blood was collected in PAXGene tubes and frozen at -80oC. In this work we have looked at the effect of statins on gene expression in 620 subjects of whom 168 were statin users. ECLIPSE study was described in: Vestbo J, Anderson W, Coxson HO, et al.: Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). Eur Respir J. 2008;31(4):869-73
2015-07-23 | E-GEOD-71220 | biostudies-arrayexpress
Project description:Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)
| PRJNA354106 | ENA
Project description:<p>ECLIPSE was a longitudinal observational study of 2164 COPD subjects and a smaller number of smoking controls (337) and nonsmoking controls (245) followed regularly for three years, with three chest CT scans (at baseline, one year, and three years) (Vestbo, European Respiratory Journal 2008; 31: 869). Inclusion criteria included age 40-75, at least 10 pack-years of smoking, and spirometry in GOLD grades 2-4 (COPD cases) or normal spirometry with post-bronchodilator FEV1 >85% predicted and FEV1/FVC>0.7 (controls). Study visits were performed at enrollment, three months, and every six months thereafter with spirometry, questionnaires, and other clinical evaluations. The ECLIPSE CT scans have been analyzed with the VIDA software for emphysema and airway phenotypes. ECLIPSE has provided key insights into the clinical epidemiology of COPD, including COPD exacerbations (Hurst, NEJM 2010; 363: 1128) and lung function decline in COPD (Vestbo, NEJM 2011; 365: 1184). ECLIPSE has been used in a number of genetic studies of COPD susceptibility and protein biomarkers(Faner, Thorax 2014; 69: 666). Genome-wide gene expression microarray data are available in 147 induced sputum samples from COPD subjects and 248 peripheral blood samples from COPD and control subjects.</p>
| phs001252 | dbGaP
Project description:NHLBI TOPMed: Boston Early-Onset COPD Study
| PRJNA288739 | ENA
Project description:NHLBI TOPMed: Genetic Epidemiology of COPD (COPDGene)
| PRJNA289174 | ENA
Project description:Background: COPD is currently the fourth leading cause of death worldwide and predicted to rank third by 2020. Statins are commonly used lipid lowering agents with documented benefits on cardiovascular morbidity and mortality, and have also been shown to have pleiotropic effects including anti-inflammatory and anti-oxidant activity. Objective: Identify a gene signature associated with statin use in the blood of COPD patients, and identify molecular mechanisms and pathways underpinning this signature that could explain any potential benefits in COPD. Methods: Whole blood gene expression was measured on 168 statin users and 452 non-users from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study. Gene expression was measured using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data and to filter out non-informative probe sets. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and employing a surrogate variable analysis. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results: 18 genes were differentially expressed between statin users and non-users at a false discovery rate of 10%. Top genes included LDLR, ABCA1, ABCG1, MYLIP, SC4MOL, and DHCR24. The 18 genes were significantly enriched in pathways and biological processes related to cholesterol homeostasis and metabolism, and were enriched for transcription factor binding sites for sterol regulatory element binding protein 2 (SREBP-2). The resulting gene signature showed correlation with Huntington disease, Parkinson’s disease and acute myeloid leukemia. Conclusion: Statins gene signature was not enriched in any pathways related to respiratory diseases, beyond the drug’s effect on cholesterol homeostasis.
2015-07-23 | GSE71220 | GEO
Project description:SummaryFrangakis and Rubin (2002, Biometrics 58, 21-29) proposed a new definition of a surrogate endpoint (a "principal" surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case-cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the "surrogate value" of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection.
| S-EPMC2726718 | biostudies-literature
Project description:Recently a new definition of surrogate endpoint, the "principal surrogate," was proposed based on causal associations between treatment effects on the biomarker and on the clinical endpoint. Despite its appealing interpretation, limited research has been conducted to evaluate principal surrogates, and existing methods focus on risk models that consider a single biomarker. How to compare principal surrogate value of biomarkers or general risk models that consider multiple biomarkers remains an open research question. We propose to characterize a marker or risk model's principal surrogate value based on the distribution of risk difference between interventions. In addition, we propose a novel summary measure (the standardized total gain) that can be used to compare markers and to assess the incremental value of a new marker. We develop a semiparametric estimated-likelihood method to estimate the joint surrogate value of multiple biomarkers. This method accommodates two-phase sampling of biomarkers and is more widely applicable than existing nonparametric methods by incorporating continuous baseline covariates to predict the biomarker(s), and is more robust than existing parametric methods by leaving the error distribution of markers unspecified. The methodology is illustrated using a simulated example set and a real data set in the context of HIV vaccine trials.
| S-EPMC3163011 | biostudies-literature