The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations.
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ABSTRACT:
SUBMITTER: Ivanova A
PROVIDER: S-EPMC7954787 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Patients who are prone to exacerbations of asthma experience significant costs in terms of missed work and school, acute care visits, and hospitalizations. Exacerbations are largely driven by environmental exposures including pollutants, stress, and viral and bacterial pathogens. These exposures are most likely to induce acute severe "asthma attacks" in high-risk patients. These personal risk factors for exacerbations can vary with the phenotype of asthma and age of the patient. In children, allergic sensitization is a strong risk factor, especially for those children who develop sensitization early in life. Airway inflammation is an important risk factor, and biomarkers are under evaluation for utility in detecting eosinophilic and type 2 inflammation and neutrophilic inflammation as indicators of risk for recurrent exacerbations. Insights into inflammatory mechanisms have led to new approaches to prevent exacerbations using mAb-based biologics that target specific type 2 pathways. Challenges remain in developing an evidence base to support precision interventions with these effective yet expensive therapies, and in determining whether these treatments will be safe and effective in young children. Unfortunately, there has been less progress in developing treatments for acute exacerbations. Hopefully, greater understanding of mechanisms relating airway viruses, bacteria, mucin production, and neutrophilic inflammatory responses will lead to additional treatment options for patients experiencing acute exacerbations.
| S-EPMC6942520 | biostudies-literature
Project description:Rationale: Cross-sectional studies suggest an exacerbation-prone asthma (EPA) phenotype and the utility of blood eosinophils and plasma IL-6 as predictive biomarkers.Objectives: To prospectively test for EPA phenotype and utility of baseline blood measures of eosinophils and IL-6 as predictive biomarkers.Methods: Three-year asthma exacerbation data were analyzed in 406 adults in the Severe Asthma Research Program-3. Transition models were used to assess uninformed and informed probabilities of exacerbation in year 3. Binomial regression models were used to assess eosinophils and IL-6 as predictive biomarkers.Measurements and Main Results: Eighty-three participants (21%) had ≥1 exacerbation in each year (EPA) and 168 participants (41%) had no exacerbation in any year (exacerbation-resistant asthma). The uninformed probability of an exacerbation in Year 3 was 40%, but the informed probability increased to 63% with an exacerbation in Year 2 and 82% with an exacerbation in Years 1 and 2. The probability of a Year 3 exacerbation with no Year 1 or 2 exacerbations was 13%. Compared with exacerbation-resistant asthma, EPA was characterized by lower FEV1 and a higher prevalence of obesity, hypertension, and diabetes. High-plasma IL-6 occurred in EPA, and the incident rate ratio for exacerbation increased 10% for each 1-pg/μl increase in baseline IL-6 level. Although high blood eosinophils did not occur in EPA, the incident rate ratio for exacerbations increased 9% for each 100-cell/μl increase in baseline eosinophil number.Conclusions: Longitudinal analysis confirms an EPA phenotype characterized by features of metabolic dysfunction. Blood measures of IL-6, but not eosinophils, were significantly associated with EPA, and IL-6 and eosinophils predicted exacerbations in the sample as a whole.
| S-EPMC7528796 | biostudies-literature
Project description: Not available
| S-EPMC7528798 | biostudies-literature
Project description:BACKGROUND:Minority groups of African descent experience disproportionately greater asthma morbidity compared with other racial groups, suggesting that genetic variation from a common ancestry could influence exacerbation risk. OBJECTIVE:We evaluated clinical trial measures in the context of self-reported race and genetic ancestry to identify risk factors for asthma exacerbations. METHODS:One thousand eight hundred forty multiethnic subjects from 12 Asthma Clinical Research Network and AsthmaNet trials were analyzed for incident asthma exacerbations with Poisson regression models that included clinical measures, self-reported race (black, non-Hispanic white, and other), and estimates of global genetic African ancestry in a subgroup (n = 760). RESULTS:Twenty-four percent of 1840 subjects self-identified as black. Black and white subjects had common risk factors for exacerbations, including a history of 2 or more exacerbations in the previous year and FEV1 percent predicted values, whereas chronic sinusitis, allergic rhinitis, and gastroesophageal reflux disease were only associated with increased exacerbation risk in black subjects. In the combined multiethnic cohort, neither race (P = .30) nor percentage of genetic African ancestry as a continuous variable associated with exacerbation risk (adjusted rate ratio [RR], 1.26 [95% CI, 0.94-1.70; P = .13]; RR per 1-SD change [32% ancestry], 0.97 [95% CI, 0.78-1.19; P = .74]). However, in 161 black subjects with genetic data, those with African ancestry greater than the median (?82%) had a significantly greater risk of exacerbation (RR, 3.06 [95% CI, 1.09-8.6; P = .03]). CONCLUSION:Black subjects have unique risk factors for asthma exacerbations, of which global African genetic ancestry had the strongest effect.
| S-EPMC6931257 | biostudies-literature
Project description:Background: Some children with asthma remain poorly controlled and have recurrent exacerbations despite treatment with inhaled corticosteroids. Aside from prior exacerbations, there are currently no reliable predictors of exacerbation-prone asthma in these children and limited understanding of potential underlying mechanisms. Objective: We sought to quantify small molecules in the plasma of children with exacerbation- prone asthma through mass spectrometry-based metabolomics. We hypothesized that the plasma metabolome of these children would differ from that of children with non-exacerbation- prone asthma. Methods: Plasma metabolites were extracted from four pediatric asthma cohorts (n=215 total, n=41 with exacerbation-prone asthma) and detected using a ZIC-HILIC column coupled to a Q Exactive HF mass spectrometer. High-confidence annotations were retained for univariate analysis and were confirmed by a sensitivity analysis in subjects on high-dose inhaled corticosteroids. Metabolites that varied by cohort were excluded. Metaboanalyst was used to identify pathways of interest. Concentrations were calculated by reference standardization to NIST SRM 1950. Results: We identified 32 unique, cohort-independent metabolites that differed in children with exacerbation-prone asthma compared to children with non-exacerbation-prone asthma. Comparison of metabolite concentrations to literature-reported values for healthy children revealed that most metabolites were decreased in both asthma groups, but more so in exacerbation-prone asthma. Pathway analysis identified arginine, lysine, and methionine pathways as most impacted. Conclusions: Several plasma metabolites are perturbed in children with exacerbation-prone asthma and are largely related to arginine, lysine, and methionine pathways. While validation is needed, plasma metabolites may be potential biomarkers for exacerbation-prone asthma in children.
2022-07-25 | ST002251 | MetabolomicsWorkbench
Project description:There is a critical need for new analytics to personalize behavioral data analysis across different fields, including kinesiology, sports science, and behavioral neuroscience. Specifically, to better translate and integrate basic research into patient care, we need to radically transform the methods by which we describe and interpret movement data. Here, we show that hidden in the "noise," smoothed out by averaging movement kinematics data, lies a wealth of information that selectively differentiates neurological and mental disorders such as Parkinson's disease, deafferentation, autism spectrum disorders, and schizophrenia from typically developing and typically aging controls. In this report, we quantify the continuous forward-and-back pointing movements of participants from a large heterogeneous cohort comprising typical and pathological cases. We empirically estimate the statistical parameters of the probability distributions for each individual in the cohort and report the parameter ranges for each clinical group after characterization of healthy developing and aging groups. We coin this newly proposed platform for individualized behavioral analyses "precision phenotyping" to distinguish it from the type of observational-behavioral phenotyping prevalent in clinical studies or from the "one-size-fits-all" model in basic movement science. We further propose the use of this platform as a unifying statistical framework to characterize brain disorders of known etiology in relation to idiopathic neurological disorders with similar phenotypic manifestations.
| S-EPMC4735831 | biostudies-literature
Project description:Asthma exacerbation (AE) usually denotes worsening of asthma symptoms that requires intense management to prevent further deterioration. AE has been reported to correlate with clinical and demographic factors, such as race, gender, and treatment compliance as well as environmental factors, such as viral infection, smoking, and air pollution. In addition, recent observations suggest that there are likely to be genetic factors specific to AE. Understanding genetic factors specific to AE is essential to develop therapy tailored for exacerbation-prone asthma. Here, we summarize the results of studies involving genetic risk factors for AE. To simplify and enhance understanding, we reviewed the studies according to the following categories: hypothesis-driven approaches, hypothesis-free approaches, gene-environment interactions, and pharmacogenetics.
| S-EPMC5352570 | biostudies-literature
Project description:In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies.
| S-EPMC9253123 | biostudies-literature
Project description:Background: Clinical transcriptomics of peripheral blood mononuclear cells (PBMC) are coming into focus as a surrogate approach for prognosis, diagnosis, biomarker discovery and examination disease mechanisms. However, bioassays paired with transcriptomic analytic tools are yet to be developed and made available at point of care. Harnessing personal dynamic genomic responses to tailor patient asthma treatment or prevent disease exacerbations remain unmet medical needs. Method: We developed a rhinovirus-stimulated peripheral blood based-assay (virogram assay) coupled with single-subject analytics (N-of-1-patwhays) to capture dynamic genome-wide expression and dysregulated pathways to retrospectively predict childhood asthma exacerbation. We hypothesized that some genomic factors might predispose any given individual, healthy or asthmatic, to a set of similar transcriptional responses to rhinovirus stimulation. We first generated a classifier from paired sample microarrays, control and stimulated PBMC from healthy subjects and applied this classifier on the transcriptomic analysis of control and HRV-stimulated PBMC samples (virogram assay) from children with asthma. Results: The analysis of the different genomic responses of single-subject paired PBMC samples (HRV-stimulated and control) derived from healthy individuals (external dataset) enabled the discovery of dysregulated pathways related to acquired immunity, epigenetics and morphogenesis. The classifier built on these results and applied on the transcriptional analysis derived from the virogram assay predicted that the risk of asthma exacerbation among asthmatic subjects with an accuracy of 70%. Conclusion: We provide evidence that clinical prognosis can be predicted with a PBMC based-bioassay aligned with adequate single-subject analytics to assess dynamic transcriptomic response to specific disease-associated stimuli.
2016-05-01 | GSE68479 | GEO
Project description:BACKGROUND:Circulating microRNAs have shown promise as non-invasive biomarkers and predictors of disease activity. Prior asthma studies using clinical, biochemical and genomic data have not shown excellent prediction of exacerbation. We hypothesized that a panel of circulating microRNAs in a pediatric asthma cohort combined with an exacerbation clinical score might predict exacerbation better than the latter alone. METHODS:Serum samples from 153 children at randomization in the Childhood Asthma Management Program were profiled for 754 microRNAs. Data dichotomized for asthma exacerbation one year after randomization to inhaled corticosteroid treatment were used for binary logistic regression with miRNA expressions and exacerbation clinical score. RESULTS:12 of 125 well-detected circulating microRNAs had significant odd ratios for exacerbation with miR-206 being most significant. Each doubling of expression of the 12 microRNA corresponded to a 25-67% increase in exacerbation risk. Stepwise logistic regression yielded a 3-microRNA model (miR-146b, miR-206 and miR-720) that, combined with the exacerbation clinical score, had excellent predictive power with a 0.81 AUROC. These 3 microRNAs were involved in NF-k? and GSK3/AKT pathways. CONCLUSIONS:This combined circulating microRNA-clinical score model predicted exacerbation in asthmatic subjects on inhaled corticosteroids better than each constituent feature alone. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT00000575 .
| S-EPMC6020199 | biostudies-literature