12 research outputs found

    Bacterial Signatures of Paediatric Respiratory Disease : An Individual Participant Data Meta-Analysis

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    Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.Peer reviewe

    Liquefied petroleum gas or biomass for cooking and effects on birth weight

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    BACKGROUND: Exposure during pregnancy to household air pollution caused by the burning of solid biomass fuel is associated with adverse health outcomes, including low birth weight. Whether the replacement of a biomass cookstove with a liquefied petroleum gas (LPG) cookstove would result in an increase in birth weight is unclear. METHODS: We performed a randomized, controlled trial involving pregnant women (18 to <35 years of age and at 9 to <20 weeks’ gestation as confirmed on ultrasonography) in Guatemala, India, Peru, and Rwanda. The women were assigned in a 1:1 ratio to use a free LPG cookstove and fuel (intervention group) or to continue using a biomass cookstove (control group). Birth weight, one of four prespecified primary outcomes, was the primary outcome for this report; data for the other three outcomes are not yet available. Birth weight was measured within 24 hours after birth. In addition, 24-hour personal exposures to fine particulate matter (particles with a diameter of ≤2.5 μm [PM2.5]), black carbon, and carbon monoxide were measured at baseline and twice during pregnancy. RESULTS: A total of 3200 women underwent randomization; 1593 were assigned to the intervention group, and 1607 to the control group. Uptake of the intervention was nearly complete, with traditional biomass cookstoves being used at a median rate of less than 1 day per month. After randomization, the median 24-hour personal exposure to fine particulate matter was 23.9 μg per cubic meter in the intervention group and 70.7 μg per cubic meter in the control group. Among 3061 live births, a valid birth weight was available for 94.9% of the infants born to women in the intervention group and for 92.7% of infants born to those in the control group. The mean (±SD) birth weight was 2921±474.3 g in the intervention group and 2898±467.9 g in the control group, for an adjusted mean difference of 19.6 g (95% confidence interval, −10.1 to 49.2). CONCLUSIONS: The birth weight of infants did not differ significantly between those born to women who used LPG cookstoves and those born to women who used biomass cookstoves. (Funded by the National Institutes of Health and the Bill and Melinda Gates Foundation; HAPIN ClinicalTrials.gov number, NCT02944682. opens in new tab.

    Infection, Inflammation, and Lung Function Decline in Infants with Cystic Fibrosis

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    Rationale: Better understanding of evolution of lung function in infants with cystic fibrosis (CF) and its association with pulmonary inflammation and infection is crucial in informing both early intervention studies aimed at limiting lung damage and the role of lung function as outcomes in such studies. Objectives: To describe longitudinal change in lung function in infants with CF and its association with pulmonary infection and inflammation. Methods: Infants diagnosed after newborn screening or clinical presentation were recruited prospectively. FVC, forced expiratory volume in 0.5 seconds (FEV0.5), and forced expiratory flows at 75% of exhaled vital capacity (FEF75) were measured using the raised-volume technique, and z-scores were calculated from published reference equations. Pulmonary infection and inflammation were measured in bronchoalveolar lavage within 48 hours of lung function testing. Measurements and Main Results: Thirty-seven infants had at least two successful repeat lung function measurements. Mean (SD) z-scores for FVC were 130.8 (1.0), 130.9 (1.1), and 131.7 (1.2) when measured at the first visit, 1-year visit, or 2-year visit, respectively. Mean (SD) z-scores for FEV0.5 were 131.4 (1.2), 132.4 (1.1), and 134.3 (1.6), respectively. In those infants in whom free neutrophil elastase was detected, FVC z-scores were 0.81 lower (P = 0.003), and FEV0.5 z-scores 0.96 lower (P = 0.001), respectively. Significantly greater decline in FEV0.5 z-scores occurred in those infected with Staphylococcus aureus (P = 0.018) or Pseudomonas aeruginosa (P = 0.021). Conclusions: In infants with CF, pulmonary inflammation is associated with lower lung function, whereas pulmonary infection is associated with a greater rate of decline in lung function. Strategies targeting pulmonary inflammation and infection are required to prevent early decline in lung function in infants with CF

    Chronic suppurative lung disease and bronchiectasis in children and adults in Australia and New Zealand

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    Guidelines on managing chronic suppurative lung disease (CSLD) and bronchiectasis (unrelated to cystic fi brosis [CF]) in Australian Indigenous children initiated in 20021 were extended to include Indigenous adults in 20082 and children and adults living in urban areas of Australia and New Zealand in 2010.3 Here, we present an updated guideline relevant for all sections of the community. The recommendations in this guideline are targeted principally to primary and secondary care, and are not intended for individualised specialist care. As with all guidelines, they are not a substitute for sound clinical judgement, particularly when investigating and treating such a phenotypically heterogeneous condition as bronchiectasis

    Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis

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    International audienceIntroduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha-and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis
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