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

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

Early bronchiectasis in cystic fibrosis detected by surveillance CT

There is emerging evidence that cystic fibrosis lung disease begins early in infancy. Newborn screening allows early detection and surveillance of pulmonary disease and the possibility of early intervention in this life-shortening condition.We report two children with cystic fibrosis who underwent a comprehensive assessment from diagnosis that included measurement of lung function, limited-slice high-resolution CT and BAL performed annually. Early aggressive surveillance enabled significant lung disease and bronchiectasis to be detected during the first few years of life and led to a change in management, highlighting a clinical role for CT scanning during the preschool years in children with cystic fibrosis

Heat Exposure among Adult Women in Rural Tamil Nadu, India.

Exposure to heat is associated with a substantial burden of disease and is an emerging issue in the context of climate change. Heat is of particular concern in India, which is one of the worlds hottest countries and also most populous, where relatively little is known about personal heat exposure, particularly in rural areas. Here, we leverage data collected as part of a randomized controlled trial to describe personal temperature exposures of adult women (40-79 years of age) in rural Tamil Nadu. We also characterize measurement error in heat exposure assessment by comparing personal exposure measurements to the nearest ambient monitoring stations and to commonly used modeled temperature data products. We find that temperatures differ across individuals in the same area on the same day, sometimes by more than 5 °C within the same hour, and that some individuals experience sharp increases in heat exposure in the early morning or evening, potentially a result of cooking with solid fuels. We find somewhat stronger correlations between the personal exposure measurements and the modeled products than with ambient monitors. We did not find evidence of systematic biases, which indicates that adjusting for discrepancies between different exposure measurement methods is not straightforward

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

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

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

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

Comparison of next-generation portable pollution monitors to measure exposure to PM2.5 from household air pollution in Puno, Peru

Assessment of personal exposure to PM2.5 is critical for understanding intervention effectiveness and exposure-response relationships in household air pollution studies. In this pilot study, we compared PM2.5 concentrations obtained from two next-generation personal exposure monitors (the Enhanced Children MicroPEM or ECM; and the Ultrasonic Personal Air Sampler or UPAS) to those obtained with a traditional Triplex Cyclone and SKC Air Pump (a gravimetric cyclone/pump sampler). We co-located cyclone/pumps with an ECM and UPAS to obtain 24-hour kitchen concentrations and personal exposure measurements. We measured Spearmen correlations and evaluated agreement using the Bland-Altman method. We obtained 215 filters from 72 ECM and 71 UPAS co-locations. Overall, the ECM and the UPAS had similar correlation (ECM rho = 0.91 vs UPAS rho = 0.88) and agreement (ECM mean difference of 121.7 mu g/m(3) vs UPAS mean difference of 93.9 mu g/m(3)) with overlapping confidence intervals when compared against the cyclone/pump. When adjusted for the limit of detection, agreement between the devices and the cyclone/pump was also similar for all samples (ECM mean difference of 68.8 mu g/m(3) vs UPAS mean difference of 65.4 mu g/m(3)) and personal exposure samples (ECM mean difference of -3.8 mu g/m(3) vs UPAS mean difference of -12.9 mu g/m(3)). Both the ECM and UPAS produced comparable measurements when compared against a cyclone/pump setup