HYDROXYTYROSOL IN CARDIOVASCULAR RISK
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB84474 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Platelet Reactivity ExpreSsion Score Predicts Cardiovascular Risk
| PRJNA970668 | ENA
Project description:Platelets are key mediators of atherothrombosis; yet, limited tools exist to identify individuals with a hyperreactive platelet phenotype. Here we derive and validate a Platelet Reactivity ExpreSsion Score (PRESS) integrating platelet aggregation responses and RNA sequencing in multiple cohorts. PRESS performs well in identifying a hyperreactive phenotype in patients with cardiovascular disease (AUC [cross-validation] 0.81, 95% CI 0.68 to 0.94), with similar discrimination in an independent cohort of healthy participants (AUC [validation] 0.77, 95% CI 0.75 to 0.79); accuracy 80%, sensitivity 71%, and specificity 86%. Next, we validate PRESS in multiple cohorts of patients with platelet RNASeq available. Following multivariable adjustment, PRESS was significantly higher in MI (β=1.8, p=0.02), and individuals with a score above the median more frequently develop a future cardiovascular event (adjusted HR 1.90, CI 1.07 to 3.36), p=0.027). Altogether, we provide strong evidence that PRESS is a robust prognostic marker, which could have an essential role in facilitating a personalized approach to cardiovascular risk management.
2024-06-30 | GSE232027 | GEO
Project description:Background: Identifying individuals at heightened cardiovascular risk is a priority for reducing the global burden of cardiovascular disease. Aspirin is widely used to prevent cardiovascular events, though with variable results. Therefore, we hypothesized that aspirin exposure would reveal novel biological pathways relevant to the development of cardiovascular events. Methods: We administered aspirin, followed by peripheral blood RNA microarray profiling, in a discovery cohort of healthy volunteers (n = 50, HV1), followed by two validation cohorts of healthy volunteers (n = 53, HV2) or outpatient cardiology (OPC, n = 25) patients, in conjunction with platelet function testing with the platelet functions score (PFS, HV1 and HV2) or the VerifyNow Asprin (VN, OPC) test. Sets of coexpressed genes, or “Factors” were identified via Bayesian sparse factor analysis and associated with platelet function in HV1 and validated in HV2 and OPC. Validated factors were associated with death/MI in observational (n = 191) and case:control (n = 447) patient cohorts with available RNA data collected at the time of cardiac catheterization. Results: Factor analysis yielded 20 Factors, of which one, Factor 14, contained 60 genes and was associated with PFS in HV1 (r = -0.31, p-value = 0.03). Factor 14 was associated with platelet function with the same strength and direction in HV2 (r = -0.34, p-value = 0.02) and OPC (one-sided p-value for aspirin resistant vs. aspirin sensitive = 0.046), thus validating the association. Factor 14 was associated with death/MI in the two patient cohorts, odds ratio (OR) = 1.2, 95% CI [1.02-1.4], p-value = 0.01 and hazard ratio = 1.5, [1.2-1.9], p = 0.001, respectively, independent of known cardiovascular risk factors (combined OR = 1.2, CI = [1.02, 1.4], p = 0.03). Factor 14 and the expression of the Factor 14 transcript most highly correlative of PFS, ITGA2B, improved reclassification compared to traditional risk factors (category-free net reclassification index = 31% and 37%, p ≤ 0.0002 for both). Conclusions: By challenging humans subjects with aspirin, a medication used for cardiovascular risk reduction, we elucidated genes and pathways that may underlie platelet function and mechanisms responsible for cardiovascular death/MI. This accession represents the OPC cohort microarray data 26 subjects selected for microarray analysis were divided into three groups: aspirin resistant (AR, n=8, >550 ARU), high normal (HN, n=9, ARU 500-549) and aspirin sensitive (AS, n=9, ARU<500)
2013-12-06 | E-GEOD-38511 | biostudies-arrayexpress
Project description:Background: Identifying individuals at heightened cardiovascular risk is a priority for reducing the global burden of cardiovascular disease. Aspirin is widely used to prevent cardiovascular events, though with variable results. Therefore, we hypothesized that aspirin exposure would reveal novel biological pathways relevant to the development of cardiovascular events. Methods: We administered aspirin, followed by peripheral blood RNA microarray profiling, in a discovery cohort of healthy volunteers (n = 50, HV1), followed by two validation cohorts of healthy volunteers (n = 53, HV2) or outpatient cardiology (OPC, n = 25) patients, in conjunction with platelet function testing with the platelet functions score (PFS, HV1 and HV2) or the VerifyNow Asprin (VN, OPC) test. Sets of coexpressed genes, or “Factors” were identified via Bayesian sparse factor analysis and associated with platelet function in HV1 and validated in HV2 and OPC. Validated factors were associated with death/MI in observational (n = 191) and case:control (n = 447) patient cohorts with available RNA data collected at the time of cardiac catheterization. Results: Factor analysis yielded 20 Factors, of which one, Factor 14, contained 60 genes and was associated with PFS in HV1 (r = -0.31, p-value = 0.03). Factor 14 was associated with platelet function with the same strength and direction in HV2 (r = -0.34, p-value = 0.02) and OPC (one-sided p-value for aspirin resistant vs. aspirin sensitive = 0.046), thus validating the association. Factor 14 was associated with death/MI in the two patient cohorts, odds ratio (OR) = 1.2, 95% CI [1.02-1.4], p-value = 0.01 and hazard ratio = 1.5, [1.2-1.9], p = 0.001, respectively, independent of known cardiovascular risk factors (combined OR = 1.2, CI = [1.02, 1.4], p = 0.03). Factor 14 and the expression of the Factor 14 transcript most highly correlative of PFS, ITGA2B, improved reclassification compared to traditional risk factors (category-free net reclassification index = 31% and 37%, p ≤ 0.0002 for both). Conclusions: By challenging humans subjects with aspirin, a medication used for cardiovascular risk reduction, we elucidated genes and pathways that may underlie platelet function and mechanisms responsible for cardiovascular death/MI. This accession represents the OPC cohort microarray data
2013-12-06 | GSE38511 | GEO
Project description:Adverse lung effects in rodents following pulmonary exposure to multi-walled carbon nanotubes (MWCNT) are well documented. However, systemic effects are less understood. Prospective epidemiological studies have shown increased cardiovascular disease risk after pulmonary exposure to airborne particles, which has led to concerns that inhalation exposure to MWCNT might pose similar risks. We used high-content genomics tools to compare hepatic responses after exposure to a short, entangled MWCNT to the hepatic responses after exposure to a long, stiffer MWCNT at the global transcriptomic level. Female C57BL/6 mice were exposed by single intratracheal instillation to 162 M-BM-5g/mouse of a short MWCNT (NRCWE-26 (NC-7000), 847M-BM-1102 nm in length) or long MWCNT (NM-401 (CP-0006-SG), 4048M-BM-1366 nm in length). Liver tissues were harvested 24 h, 3 d and 28 d after exposure. This experiment examined the pulmonary transcriptional response of female C57BL/6 mice exposed to NRCWE-26, a short multi-walled carbon nanotube, and NM-401, a long multi-walled carbon nanotube, at three doses: D1 (18 M-NM-<g), D2 (54 M-NM-<g), D3 (162 M-NM-<g), and vehicle control. Each dose group was examined 1, 3 or 28 days post-exposure. Each dose group had 6 biological replicates. There were a total of 72 samples included in the final analysis using a two-color reference design.
2015-02-02 | E-GEOD-61366 | biostudies-arrayexpress
Project description:Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n=32) and serum (n=68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC=0.80. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC=0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC=0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of these patients.
2025-01-06 | GSE280768 | GEO
Project description:Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n=32) and serum (n=68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC=0.80. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC=0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC=0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of these patients.
2025-01-06 | GSE280772 | GEO
Project description:Action to Control Cardiovascular Risk in Diabetes (ACCORD-BioLINCC)
| PRJNA1075472 | ENA
Project description:Action to Control Cardiovascular Risk in Diabetes (ACCORD - Imaging)
| PRJNA1079425 | ENA
Project description:Action to Control Cardiovascular Risk in Diabetes (ACCORD - Imaging)
| PRJNA1079426 | ENA