The Journal of allergy and clinical immunology
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J. Allergy Clin. Immunol. · Mar 2017
Randomized Controlled TrialAberrant IgA responses to the gut microbiota during infancy precede asthma and allergy development.
Although a reduced gut microbiota diversity and low mucosal total IgA levels in infancy have been associated with allergy development, IgA responses to the gut microbiota have not yet been studied. ⋯ An aberrant IgA responsiveness to the gut microbiota during infancy precedes asthma and allergy development, possibly indicating an impaired mucosal barrier function in allergic children.
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J. Allergy Clin. Immunol. · Feb 2017
Meta AnalysisGenome-wide association study on the FEV1/FVC ratio in never-smokers identifies HHIP and FAM13A.
Although a striking proportion (25% to 45%) of patients with chronic obstructive pulmonary disease are never-smokers, most genetic susceptibility studies have not focused on this group exclusively. ⋯ The genes HHIP and FAM13A confer a risk for airway obstruction in general that is not driven exclusively by cigarette smoking, which is the main risk factor for chronic obstructive pulmonary disease.
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J. Allergy Clin. Immunol. · Feb 2017
Early life rhinovirus wheezing, allergic sensitization, and asthma risk at adolescence.
Early life rhinovirus (RV) wheezing illnesses and aeroallergen sensitization increase the risk of asthma at school age. Whether these remain risk factors for the persistence of asthma out to adolescence is not established. ⋯ In a high-risk birth cohort, the persistence of asthma at age 13 years was most strongly associated with outpatient wheezing illnesses with RV and aeroallergen sensitization in early life.
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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. ⋯ 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.