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      Disaggregating asthma: Big investigation versus big data

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          Abstract

          We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand causal mechanisms and translating this knowledge into personalized prevention and management strategies. In recent years, “big data” has been sold as a panacea for generating hypotheses and driving new frontiers of health care; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of health care data and computational tools for data analysis is that the process of data mining can become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data, and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data- and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological, and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness bigger health care data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma.

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          Most cited references66

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          Mepolizumab for severe eosinophilic asthma (DREAM): a multicentre, double-blind, placebo-controlled trial.

          Some patients with severe asthma have recurrent asthma exacerbations associated with eosinophilic airway inflammation. Early studies suggest that inhibition of eosinophilic airway inflammation with mepolizumab-a monoclonal antibody against interleukin 5-is associated with a reduced risk of exacerbations. We aimed to establish efficacy, safety, and patient characteristics associated with the response to mepolizumab. We undertook a multicentre, double-blind, placebo-controlled trial at 81 centres in 13 countries between Nov 9, 2009, and Dec 5, 2011. Eligible patients were aged 12-74 years, had a history of recurrent severe asthma exacerbations, and had signs of eosinophilic inflammation. They were randomly assigned (in a 1:1:1:1 ratio) to receive one of three doses of intravenous mepolizumab (75 mg, 250 mg, or 750 mg) or matched placebo (100 mL 0·9% NaCl) with a central telephone-based system and computer-generated randomly permuted block schedule stratified by whether treatment with oral corticosteroids was required. Patients received 13 infusions at 4-week intervals. The primary outcome was the rate of clinically significant asthma exacerbations, which were defined as validated episodes of acute asthma requiring treatment with oral corticosteroids, admission, or a visit to an emergency department. Patients, clinicians, and data analysts were masked to treatment assignment. Analyses were by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01000506. 621 patients were randomised: 159 were assigned to placebo, 154 to 75 mg mepolizumab, 152 to 250 mg mepolizumab, and 156 to 750 mg mepolizumab. 776 exacerbations were deemed to be clinically significant. The rate of clinically significant exacerbations was 2·40 per patient per year in the placebo group, 1·24 in the 75 mg mepolizumab group (48% reduction, 95% CI 31-61%; p<0·0001), 1·46 in the 250 mg mepolizumab group (39% reduction, 19-54%; p=0·0005), and 1·15 in the 750 mg mepolizumab group (52% reduction, 36-64%; p<0·0001). Three patients died during the study, but the deaths were not deemed to be related to treatment. Mepolizumab is an effective and well tolerated treatment that reduces the risk of asthma exacerbations in patients with severe eosinophilic asthma. GlaxoSmithKline. Copyright © 2012 Elsevier Ltd. All rights reserved.
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            Lebrikizumab treatment in adults with asthma.

            Many patients with asthma have uncontrolled disease despite treatment with inhaled glucocorticoids. One potential cause of the variability in response to treatment is heterogeneity in the role of interleukin-13 expression in the clinical asthma phenotype. We hypothesized that anti-interleukin-13 therapy would benefit patients with asthma who had a pretreatment profile consistent with interleukin-13 activity. We conducted a randomized, double-blind, placebo-controlled study of lebrikizumab, a monoclonal antibody to interleukin-13, in 219 adults who had asthma that was inadequately controlled despite inhaled glucocorticoid therapy. The primary efficacy outcome was the relative change in prebronchodilator forced expiratory volume in 1 second (FEV(1)) from baseline to week 12. Among the secondary outcomes was the rate of asthma exacerbations through 24 weeks. Patient subgroups were prespecified according to baseline type 2 helper T-cell (Th2) status (assessed on the basis of total IgE level and blood eosinophil count) and serum periostin level. At baseline, patients had a mean FEV(1) that was 65% of the predicted value and were taking a mean dose of inhaled glucocorticoids of 580 μg per day; 80% were also taking a long-acting beta-agonist. At week 12, the mean increase in FEV(1) was 5.5 percentage points higher in the lebrikizumab group than in the placebo group (P = 0.02). Among patients in the high-periostin subgroup, the increase from baseline FEV(1) was 8.2 percentage points higher in the lebrikizumab group than in the placebo group (P = 0.03). Among patients in the low-periostin subgroup, the increase from baseline FEV(1) was 1.6 percentage points higher in the lebrikizumab group than in the placebo group (P = 0.61). Musculoskeletal side effects were more common with lebrikizumab than with placebo (13.2% vs. 5.4%, P = 0.045). Lebrikizumab treatment was associated with improved lung function. Patients with high pretreatment levels of serum periostin had greater improvement in lung function with lebrikizumab than did patients with low periostin levels. (Funded by Genentech; ClinicalTrials.gov number, NCT00930163 .).
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              Endotyping asthma: new insights into key pathogenic mechanisms in a complex, heterogeneous disease.

              Clinical asthma is very widely assumed to be the net result of excessive inflammation driven by aberrant T-helper-2 (Th2) immunity that leads to inflamed, remodelled airways and then functional derangement that, in turn, causes symptoms. This notion of disease is actually poorly supported by data, and there are substantial discrepancies and very poor correlation between inflammation, damage, functional impairment, and degree of symptoms. Furthermore, this problem is compounded by the poor understanding of the heterogeneity of clinical disease. Failure to recognise and discover the underlying mechanisms of these major variants or endotypes of asthma is, arguably, the major intellectual limitation to progress at present. Fortunately, both clinical research and animal models are very well suited to dissecting the cellular and molecular basis of disease endotypes. This approach is already suggesting entirely novel pathways to disease-eg, alternative macrophage specification, steroid refractory innate immunity, the interleukin-17-regulatory T-cell axis, epidermal growth factor receptor co-amplification, and Th2-mimicking but non-T-cell, interleukins 18 and 33 dependent processes that can offer unexpected therapeutic opportunities for specific patient endotypes.
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                Author and article information

                Contributors
                Journal
                J Allergy Clin Immunol
                J. Allergy Clin. Immunol
                The Journal of Allergy and Clinical Immunology
                Mosby
                0091-6749
                1097-6825
                1 February 2017
                February 2017
                : 139
                : 2
                : 400-407
                Affiliations
                [a ]Department of Paediatrics, Imperial College, London, United Kingdom
                [b ]School of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, Bristol, United Kingdom
                [c ]Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
                [d ]Health Informatics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
                [e ]Microsoft Research, Cambridge, United Kingdom
                Author notes
                []Corresponding author: Adnan Custovic, MD, PhD, FAAAI, Imperial College London, Department of Paediatrics, St Mary's Campus Medical School, London W2 1PG, United Kingdom.Imperial College LondonDepartment of PaediatricsSt Mary's Campus Medical SchoolLondonW2 1PGUnited Kingdom a.custovic@ 123456imperial.ac.uk
                Article
                S0091-6749(16)31345-8
                10.1016/j.jaci.2016.11.003
                5292995
                27871876
                2d85e58b-9956-4b0b-85d0-2b3f614d0282
                © 2016 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 September 2016
                : 7 November 2016
                : 9 November 2016
                Categories
                Reviews and Feature Article

                Immunology
                asthma,endotypes,machine learning,big data,birth cohorts,stelar, study team for early life asthma research

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