Annals of the American Thoracic Society
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Multicenter Study Comparative Study
Environmental risks for nontuberculous mycobacteria. Individual exposures and climatic factors in the cystic fibrosis population.
Persons with cystic fibrosis are at high risk of pulmonary nontuberculous mycobacterial infection, with a national prevalence estimated at 13%. The risk of nontuberculous mycobacteria associated with specific environmental exposures, and the correlation with climatic conditions in this population has not been described. ⋯ Atmospheric conditions explain more of the variation in disease prevalence than individual behaviors. The risk of specific exposures may vary by geographic region due to differences in conditions favoring mycobacterial growth and survival. However, because exposure to these organisms is ubiquitous and behaviors are similar among persons with and without pulmonary nontuberculous mycobacteria, genetic susceptibility beyond cystic fibrosis is likely to be important for disease development. Common individual risk factors in high-risk populations remain to be identified.
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Randomized Controlled Trial Comparative Study
Cluster analysis and characterization of response to mepolizumab. A step closer to personalized medicine for patients with severe asthma.
Detailed characterization of asthma phenotypes is essential for identification of responder populations to allow directed personalized medical intervention. ⋯ Using supervised cluster analysis helped identify specific patient characteristics related to disease and therapeutic response. Patients with eosinophilic inflammation received significant therapeutic benefit with mepolizumab, and responses differed within clusters. Clinical trial registered with www.clinicaltrials.gov (NCT01000506).
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Comparative Study
Three clinically distinct chronic pediatric airway infections share a common core microbiota.
DNA-based microbiological studies are moving beyond studying healthy human microbiota to investigate diverse infectious diseases, including chronic respiratory infections, such as those in the airways of people with cystic fibrosis (CF) and non-CF bronchiectasis. The species identified in the respiratory secretion microbiota from such patients can be classified into those that are common and abundant among similar subjects (core) versus those that are infrequent and rare (satellite). This categorization provides a vital foundation for investigating disease pathogenesis and improving therapy. However, whether the core microbiota of people with different respiratory diseases, which are traditionally associated with specific culturable pathogens, are unique or shared with other chronic infections of the lower airways is not well studied. Little is also known about how these chronic infection microbiota change from childhood to adulthood. ⋯ Our results indicate that these clinically distinct chronic airway infections share common early core microbiota, which are likely shaped by natural aspiration and impaired clearance of the same airway microbes, but that disease-specific characteristics select for divergent microbiota by adulthood. Longitudinal and interventional studies will be required to define the relationships between microbiota, treatments, and disease progression.
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The Big Data movement in computer science has brought dramatic changes in what counts as data, how those data are analyzed, and what can be done with those data. Although increasingly pervasive in the business world, it has only recently begun to influence clinical research and practice. As Big Data draws from different intellectual traditions than clinical epidemiology, the ideas may be less familiar to practicing clinicians. ⋯ Second, Big Data asks different kinds of questions of data and emphasizes the usefulness of analyses that are explicitly associational but not causal. Third, Big Data brings new analytic approaches to bear on these questions. And fourth, Big Data embodies a new set of aspirations for a breaking down of distinctions between research data and operational data and their merging into a continuously learning health system.