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)

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

Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyteÂ® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyteÂ® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyteÂ® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyteÂ® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyteÂ® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyteÂ® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator

Project description:Importance: Severe alcoholic hepatitis (AH) is a highly lethal disease with a 3-month mortality up to 50%. Corticosteroids are the current standard of care although nearly 40% of the patients do not respond and accurate pre-treatment predictors of survival are lacking. Objective: To develop a prognostic score based on molecular and clinical variables before initiation of corticosteroids. Design, Setting, and Participants: Gene expression profiling of fixed liver biopsy obtained for diagnosis of severe AH assessed in 3 independent cohorts (1 prospective cohort for prognostic gene signature and integrative score derivation, and 2 retrospective cohorts for validation). Samples were obtained from 4 European and 1 US medical centers between July 2006 and February 2015. Exposures: Biopsy proven severe AH patients treated with corticosteroids. Main Outcome Measures: Identification of a gene signature combined with a validated clinical index to develop an integrative prognostic score of survival without death or liver transplantation in a derivation cohort (n=71). Prognostic performance of the score was validated in an independent multi-center validation cohort 1 (n=48). Finally, the score was implemented in an FDA-approved clinical diagnostic platform (NanoString) and tested in another independent validation cohort 2 (n=20). Results: A prognostic score, integrating a 123-gene signature and the model for end-stage liver disease (gs-MELD score), was defined in the derivation cohort, in which poor- and good-prognosis patients were discriminated with a cut-off value of 2.66 at 3 months (event-free survival rates of 32% vs. 76%, respectively, p<.001) and 6 months (26% vs. 65%, respectively, p<.001). In the validation cohort 1, the score similarly discriminated poor- and good-prognosis patients at 3 months (43%, [95% CI, 26%-70%] vs. 96% [95% CI, 89%-100%], respectively, p<.001, c-index of 0.83 [95% CI, 0.66-0.99]) and 6 months (34% [95% CI, 18%-61%] vs 84% [95% CI, 72%-100%], respectively, p<.001, c-index of 0.80 [95% CI, 0.66-0.94]), and outperformed other existing clinical indices. The NanoString-based gs-MELD score remained predictive of 3- (p=.03) and 6-month (p=.009) even-free survival in the validation cohort 2. Conclusion and Relevance: The gs-MELD score, incorporating clinically applicable gene signature test and the MELD score, enables pre-treatment survival prediction in severe AH patients.

Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyte® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples

Project description:Abstract: Background: The severity of visceral adipose tissue (VAT) inflammation in individuals with obesity is thought to signify obesity sub-phenotype(s) associated with higher cardiometabolic risk. Yet, this tissue is not accessible for direct sampling in the non-surgical patient. We hypothesized that circulating miRNAs (circ-miRs) could serve as biomarkers to distinguish human obesity subgroups with high or low extent of VAT-inflammation. Methods: Discovery and validation cohorts of patients living with obesity undergoing bariatric surgery (n=35 and 51, respectively) were included. VAT inflammation was classified into low/high based on an expression score derived from the mRNA levels of TNFA, IL6 and CCL2 (determined by rtPCR). Differentially-expressed circ-miRs were identified, and their discriminative power to detect low/high VAT inflammation was assessed by ROC-AUC analysis. Results: Fifty three out of 263 circ-miRs (20%) were associated with high VAT inflammation according to Mann-Whitney analysis in the discovery cohort. Of those, 12 (12/53=23%) were differentially expressed according to Deseq2, and 6 significantly discriminated between high and low VAT inflammation with ROC-AUC>0.8. Of the resulting 5 circ-miRs that were differentially abundant in all three statistical approaches, 3 were unaffected by hemolysis and validated in an independent cohort. Circ-miRs 181b-5p, 1306-3p, and 3138 combined with HOMA-IR exhibited ROC-AUC of 0.951 (95%CI:0.865-1) and 0.808 (95%CI:0.654-0.963) in the discovery and validation cohorts, respectively, providing strong discriminative power between participants with low versus high VAT inflammation. Predicted target genes of these miRNAs are enriched in pathways of insulin and inflammatory signaling, circadian entrainment, and cellular senescence. Conclusions: Circ-miRs that identify patients with low versus high VAT inflammation constitute a putative tool to improve personalized care of patients with obesity.

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Platelet Reactivity ExpreSsion Score Predicts Cardiovascular Risk

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