The level of anticoagulation in response to a fixed-dose regimen of warfarin is difficult to predict during the initiation of therapy. We prospectively compared the effect of genotype-guided dosing ...with that of standard dosing on anticoagulation control in patients starting warfarin therapy.
We conducted a multicenter, randomized, controlled trial involving patients with atrial fibrillation or venous thromboembolism. Genotyping for CYP2C9*2, CYP2C9*3, and VKORC1 (-1639G→A) was performed with the use of a point-of-care test. For patients assigned to the genotype-guided group, warfarin doses were prescribed according to pharmacogenetic-based algorithms for the first 5 days. Patients in the control (standard dosing) group received a 3-day loading-dose regimen. After the initiation period, the treatment of all patients was managed according to routine clinical practice. The primary outcome measure was the percentage of time in the therapeutic range of 2.0 to 3.0 for the international normalized ratio (INR) during the first 12 weeks after warfarin initiation.
A total of 455 patients were recruited, with 227 randomly assigned to the genotype-guided group and 228 assigned to the control group. The mean percentage of time in the therapeutic range was 67.4% in the genotype-guided group as compared with 60.3% in the control group (adjusted difference, 7.0 percentage points; 95% confidence interval, 3.3 to 10.6; P<0.001). There were significantly fewer incidences of excessive anticoagulation (INR ≥4.0) in the genotype-guided group. The median time to reach a therapeutic INR was 21 days in the genotype-guided group as compared with 29 days in the control group (P<0.001).
Pharmacogenetic-based dosing was associated with a higher percentage of time in the therapeutic INR range than was standard dosing during the initiation of warfarin therapy. (Funded by the European Commission Seventh Framework Programme and others; ClinicalTrials.gov number, NCT01119300.).
With the advancement of high‐throughput DNA/RNA sequencing and computational analysis techniques, commensal bacteria are now considered almost as important as pathological ones. Understanding the ...interaction between these bacterial microbiota, host and asthma is crucial to reveal their role in asthma pathophysiology. Several airway and/or gut microbiome studies have shown associations between certain bacterial taxa and asthma. However, challenges remain before gained knowledge from these studies can be implemented into clinical practice, such as inconsistency between studies in choosing sampling compartments and/or sequencing approaches, variability of results in asthma studies, and not taking into account medication intake and diet composition especially when investigating gut microbiome. Overcoming those challenges will help to better understand the complex asthma disease process. The therapeutic potential of using pro‐ and prebiotics to prevent or reduce risk of asthma exacerbations requires further investigation. This review will focus on methodological issues regarding setting up a microbiome study, recent developments in asthma bacterial microbiome studies, challenges and future therapeutic potential.
Background
The use of antibiotic therapy early in life might influence the risk of developing asthma. Studies assessing the influence of early life antibiotic use on the risk of asthma exacerbations ...are limited, and the results are inconsistent. Therefore, the aim of this study was to assess the association between use of antibiotic during the first 3 years of life and the risk of developing childhood asthma and the occurrence of asthma exacerbations.
Methods
Data from four large childhood cohorts were used; two population‐based cohorts to study the risk of developing asthma: Generation R (n=7393, The Netherlands) and SEATON (n=891, Scotland, UK), and two asthma cohorts to assess the risk of asthma exacerbations: PACMAN (n=668, The Netherlands) and BREATHE (n=806, Scotland, UK). Odds ratios (ORs) were derived from logistic regression analysis within each database followed by pooling the results using a fixed‐ or random‐effect model.
Results
Antibiotic use in early life was associated with an increased risk of asthma in a meta‐analysis of the Generation R and SEATON data (OR: 2.18, 95% CI: 1.04‐4.60; I2: 76.3%). There was no association between antibiotic use in early life and risk of asthma exacerbations later in life in a meta‐analysis of the PACMAN and BREATHE data (OR: 0.93, 95% CI: 0.65‐1.32; I2: 0.0%).
Conclusion
Children treated with antibiotic in the first 3 years of life are more likely to develop asthma, but there is no evidence that the exposure to antibiotic is associated with increased risk of asthma exacerbations.
There are ample data to suggest that early‐life dysbiosis of both the gut and/or airway microbiome can predispose a child to develop along a trajectory toward asthma. Although individual studies show ...clear associations between dysbiosis and asthma development, it is less clear what (collection of) bacterial species is mechanistically responsible for the observed effects. This is partly due to issues related to the asthma diagnosis and the broad spectrum of anatomical sites, sample techniques, and analysis protocols that are used in different studies. Moreover, there is limited attention for potential differences in the genetics of individuals that would affect the outcome of the interaction between the environment and that individual. Despite these challenges, the first bacterial components were identified that are able to affect the transcriptional state of human cells, ergo the immune system. Such molecules could in the future be the basis for intervention studies that are now (necessarily) restricted to a limited number of bacterial species. For this transition, it might be prudent to develop an ex vivo human model of a local mucosal immune system to better and safer explore the impact of such molecules. With this approach, we might move beyond association toward understanding of causality.
There is an association between microbial dysbiosis, in the gut and airways, and asthma development. Microbial interventions may prevent asthma development or contribute to its cure.
Because of altered airway microbiome in asthma, we analysed the bacterial species in sputum of patients with severe asthma.
Whole genome sequencing was performed on induced sputum from non-smoking ...(SAn) and current or ex-smoker (SAs/ex) severe asthma patients, mild/moderate asthma (MMA) and healthy controls (HC). Data were analysed by asthma severity, inflammatory status and transcriptome-associated clusters (TACs).
α-diversity at the species level was lower in SAn and SAs/ex, with an increase in Haemophilus influenzae and Moraxella catarrhalis, and Haemophilus influenzae and Tropheryma whipplei, respectively, compared to HC. In neutrophilic asthma, there was greater abundance of Haemophilus influenzae and Moraxella catarrhalis and in eosinophilic asthma, Tropheryma whipplei was increased. There was a reduction in α-diversity in TAC1 and TAC2 that expressed high levels of Haemophilus influenzae and Tropheryma whipplei, and Haemophilus influenzae and Moraxella catarrhalis, respectively, compared to HC. Sputum neutrophils correlated positively with Moraxella catarrhalis and negatively with Prevotella, Neisseria and Veillonella species and Haemophilus parainfluenzae. Sputum eosinophils correlated positively with Tropheryma whipplei which correlated with pack-years of smoking. α- and β-diversities were stable at one year.
Haemophilus influenzae and Moraxella catarrhalis were more abundant in severe neutrophilic asthma and TAC2 linked to inflammasome and neutrophil activation, while Haemophilus influenzae and Tropheryma whipplei were highest in SAs/ex and in TAC1 associated with highest expression of IL-13 type 2 and ILC2 signatures with the abundance of Tropheryma whipplei correlating positively with sputum eosinophils. Whether these bacterial species drive the inflammatory response in asthma needs evaluation.
Observational evidence suggests that the use of a genotype-guided dosing algorithm may increase the effectiveness and safety of acenocoumarol and phenprocoumon therapy.
We conducted two single-blind, ...randomized trials comparing a genotype-guided dosing algorithm that included clinical variables and genotyping for CYP2C9 and VKORC1 with a dosing algorithm that included only clinical variables, for the initiation of acenocoumarol or phenprocoumon treatment in patients with atrial fibrillation or venous thromboembolism. The primary outcome was the percentage of time in the target range for the international normalized ratio (INR; target range, 2.0 to 3.0) in the 12-week period after the initiation of therapy. Owing to low enrollment, the two trials were combined for analysis. The primary outcome was assessed in patients who remained in the trial for at least 10 weeks.
A total of 548 patients were enrolled (273 patients in the genotype-guided group and 275 in the control group). The follow-up was at least 10 weeks for 239 patients in the genotype-guided group and 245 in the control group. The percentage of time in the therapeutic INR range was 61.6% for patients receiving genotype-guided dosing and 60.2% for those receiving clinically guided dosing (P=0.52). There were no significant differences between the two groups for several secondary outcomes. The percentage of time in the therapeutic range during the first 4 weeks after the initiation of treatment in the two groups was 52.8% and 47.5% (P=0.02), respectively. There were no significant differences with respect to the incidence of bleeding or thromboembolic events.
Genotype-guided dosing of acenocoumarol or phenprocoumon did not improve the percentage of time in the therapeutic INR range during the 12 weeks after the initiation of therapy. (Funded by the European Commission Seventh Framework Programme and others; EU-PACT ClinicalTrials.gov numbers, NCT01119261 and NCT01119274.).
More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. ...Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired—presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.
Background
Early detection/prediction of flare‐ups in asthma, commonly triggered by viruses, would enable timely treatment. Previous studies on exhaled breath analysis by electronic nose (eNose) ...technology could discriminate between stable and unstable episodes of asthma, using single/few time‐points. To investigate its monitoring properties during these episodes, we examined day‐to‐day fluctuations in exhaled breath profiles, before and after a rhinovirus‐16 (RV16) challenge, in healthy and asthmatic adults.
Methods
In this proof‐of‐concept study, 12 atopic asthmatic and 12 non‐atopic healthy adults were prospectively followed thrice weekly, 60 days before, and 30 days after a RV16 challenge. Exhaled breath profiles were detected using an eNose, consisting of 7 different sensors. Per sensor, individual means were calculated using pre‐challenge visits. Absolute deviations (|%|) from this baseline were derived for all visits. Within‐group comparisons were tested with Mann‐Whitney U tests and receiver operating characteristic (ROC) analysis. Finally, Spearman's correlations between the total change in eNose deviations and fractional exhaled nitric oxide (FeNO), cold‐like symptoms, and pro‐inflammatory cytokines were examined.
Results
Both groups had significantly increased eNose fluctuations post‐challenge, which in asthma started 1 day post‐challenge, before the onset of symptoms. Discrimination between pre‐ and post‐challenge reached an area under the ROC curve of 0.82 (95% CI = 0.65–0.99) in healthy and 0.97 (95% CI = 0.91–1.00) in asthmatic adults. The total change in eNose deviations moderately correlated with IL‐8 and TNFα (ρ ≈ .50–0.60) in asthmatics.
Conclusion
Electronic nose fluctuations rapidly increase after a RV16 challenge, with distinct differences between healthy and asthmatic adults, suggesting that this technology could be useful in monitoring virus‐driven unstable episodes in asthma.
Electronic nose sensor fluctuations increased after a RV‐16 challenge in asthmatic and healthy adults, more evidently in asthma. In asthma, the increase in eNose fluctuations occurred before the onset of cold‐like symptoms. The magnitude of the change in eNose fluctuations was not correlated with FeNO and cold‐like symptoms, but moderately correlated with pre‐ and post‐challenge cytokine levels in asthma.
Treating severe asthma: Targeting the IL‐5 pathway Principe, Stefania; Porsbjerg, Celeste; Bolm Ditlev, Sisse ...
Clinical and experimental allergy,
August 2021, Letnik:
51, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Severe asthma is a heterogeneous disease with different phenotypes based on clinical, functional or inflammatory parameters. In particular, the eosinophilic phenotype is associated with type 2 ...inflammation and increased levels of interleukin (IL)‐4, IL‐5 and IL‐13). Monoclonal antibodies that target the eosinophilic inflammatory pathways (IL‐5R and IL‐5), namely mepolizumab, reslizumab, and benralizumab, are effective and safe for severe eosinophilic asthma. Eosinophils threshold represents the most indicative biomarker for response to treatment with all three monoclonal antibodies. Improvement in asthma symptoms scores, lung function, the number of exacerbations, history of late‐onset asthma, chronic rhinosinusitis with nasal polyposis, low oral corticosteroids use and low body mass index represent predictive clinical markers of response. Novel Omics studies are emerging with proteomics data and exhaled breath analyses. These may prove useful as biomarkers of response and non‐response biologics. Moreover, future biomarker studies need to be undertaken in paediatric patients affected by severe asthma. The choice of appropriate biologic therapy for severe asthma remains challenging. The importance of finding biomarkers that can predict response continuous an open issue that needs to be further explored. This review describes the clinical effects of targeting the IL‐5 pathway in severe asthma in adult and paediatric patients, focusing on predictors of response and non‐response.
Some patients prescribed flucloxacillin (~ 0.01%) develop drug‐induced liver injury (DILI). HLA‐B*57:01 is an established genetic risk factor for flucloxacillin DILI. To consolidate this finding, ...identify additional genetic factors, and assess relevance of risk factors for flucloxacillin DILI in relation to DILI due to other penicillins, we performed a genomewide association study involving 197 flucloxacillin DILI cases and 6,835 controls. We imputed single‐nucleotide polymorphism and human leukocyte antigen (HLA) genotypes. HLA‐B*57:01 was the major risk factor (allelic odds ratio (OR) = 36.62; P = 2.67 × 10−97). HLA‐B*57:03 also showed an association (OR = 79.21; P = 1.2 × 10−6). Within the HLA‐B protein sequence, imputation showed valine97, common to HLA‐B*57:01 and HLA‐B*57:03, had the largest effect (OR = 38.1; P = 9.7 × 10−97). We found no HLA‐B*57 association with DILI due to other isoxazolyl penicillins (n = 6) or amoxicillin (n = 15) and no significant non‐HLA signals for any penicillin‐related DILI.
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