Cross-sectional associations between air pollution and chronic bronchitis: an ESCAPE meta-analysis across five cohorts

BACKGROUND: This study aimed to assess associations of outdoor air pollution on prevalence of chronic bronchitis symptoms in adults in five cohort studies (Asthma-E3N, ECRHS, NSHD, SALIA, SAPALDIA) participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. METHODS: Annual average particulate matter (PM10, PM2.5, PMabsorbance, PMcoarse), NO2, nitrogen oxides (NOx) and road traffic measures modelled from ESCAPE measurement campaigns 2008-2011 were assigned to home address at most recent assessments (1998-2011). Symptoms examined were chronic bronchitis (cough and phlegm for ≥3 months of the year for ≥2 years), chronic cough (with/without phlegm) and chronic phlegm (with/without cough). Cohort-specific cross-sectional multivariable logistic regression analyses were conducted using common confounder sets (age, sex, smoking, interview season, education), followed by meta-analysis. RESULTS: 15 279 and 10 537 participants respectively were included in the main NO2 and PM analyses at assessments in 1998-2011. Overall, there were no statistically significant associations with any air pollutant or traffic exposure. Sensitivity analyses including in asthmatics only, females only or using back-extrapolated NO2 and PM10 for assessments in 1985-2002 (ECRHS, NSHD, SALIA, SAPALDIA) did not alter conclusions. In never-smokers, all associations were positive, but reached statistical significance only for chronic phlegm with PMcoarse OR 1.31 (1.05 to 1.64) per 5 µg/m(3) increase and PM10 with similar effect size. Sensitivity analyses of older cohorts showed increased risk of chronic cough with PM2.5abs (black carbon) exposures. CONCLUSIONS: Results do not show consistent associations between chronic bronchitis symptoms and current traffic-related air pollution in adult European populations

Mid-childhood fat mass and airflow limitation at 15 years: The mediating role of insulin resistance and C-reactive protein

Background: We previously reported an association of high fat mass levels from age 9 to 15 years with lower forced expiratory flow in 1 s (FEV1)/forced vital capacity (FVC) ratio (i.e., increased risk of airflow limitation) at 15 years. Here, we aimed to assess whether insulin resistance and C-reactive protein (CRP) at 15 years partially mediate this association. Methods: We included 2263 children from the UK Avon Longitudinal Study of Parents and Children population-based cohort (ALSPAC). Four fat mass index (FMI) trajectories (“low,” “medium-low,” “medium-high,” “high”) from 9 to 15 years were previously identified using Group-Based Trajectory Modeling. Data on CRP, glucose, insulin, and post-bronchodilator FEV1/FVC were available at 15 years. We defined insulin resistance by the homeostasis model assessment-estimated insulin resistance index (HOMA-IR). We used adjusted linear regression models and a causal mediation analysis to assess the mediating role of HOMA-IR and CRP. Results: Compared to children in the “low” FMI trajectory, children in the “medium-high” and “high” FMI trajectories had lower FEV1/FVC at 15 years. The percentage of the total effect explained by HOMA-IR was 19.8% [−114.1 to 170.0] and 20.4% [1.6 to 69.0] for the “medium-high” and “high” trajectories, respectively. In contrast, there was little evidence for a mediating role of CRP. Conclusion: The association between mid-childhood fat mass and FEV1/FVC ratio at 15 years may be partially mediated by insulin resistance

Pulmonary epithelial barrier and immunological functions at birth and in early life - key determinants of the development of asthma? A description of the protocol for the Breathing Together study

Acknowledgements The authors are indebted to the participants and parents who have already been recruited. We also acknowledge the enthusiasm and endeavour of the research nurse team which includes: Stephen Main, Margaret Connon, Catherine Beveridge, Julie Baggott, Kay Riding, Ellie McCamie, Maria Larsson, Lynda Melvin, Mumtaz Idris, Tara Murray, Nicky Tongue, Nicolene Plaatjies, Sheila Mortimer, Sally Spedding, Susy Grevatt, Victoria Welch, Morag Zelisko, Jillian Doherty, Jane Martin, Emma Macleod and Cilla Snape. We are also delighted to be working alongside the following colleagues in laboratories: Marie Craigon, Marie McWilliam, Maria Zarconi, Judit Barabas, Lindsay Broadbent, Ceyda Oksel and Sheerien Manzoor. Grant information The study is supported by the Wellcome Trust [108818]; and the PHA HSC R&D Division, Northern Ireland.Peer reviewedPublisher PD

Adult lung function and long-term air pollution exposure. ESCAPE: a multicentre cohort study and meta-analysis.

The chronic impact of ambient air pollutants on lung function in adults is not fully understood. The objective of this study was to investigate the association of long-term exposure to ambient air pollution with lung function in adult participants from five cohorts in the European Study of Cohorts for Air Pollution Effects (ESCAPE). Residential exposure to nitrogen oxides (NO\u2082, NOx) and particulate matter (PM) was modelled and traffic indicators were assessed in a standardised manner. The spirometric parameters forced expiratory volume in 1 s (FEV\u2081) and forced vital capacity (FVC) from 7613 subjects were considered as outcomes. Cohort-specific results were combined using meta-analysis. We did not observe an association of air pollution with longitudinal change in lung function, but we observed that a 10 \u3bcg\ub7m(-3) increase in NO\u2082 exposure was associated with lower levels of FEV\u2081 (-14.0 mL, 95% CI -25.8 to -2.1) and FVC (-14.9 mL, 95% CI -28.7 to -1.1). An increase of 10 \u3bcg\ub7m(-3) in PM10, but not other PM metrics (PM2.5, coarse fraction of PM, PM absorbance), was associated with a lower level of FEV\u2081 (-44.6 mL, 95% CI -85.4 to -3.8) and FVC (-59.0 mL, 95% CI -112.3 to -5.6). The associations were particularly strong in obese persons. This study adds to the evidence for an adverse association of ambient air pollution with lung function in adults at very low levels in Europe

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987

Correction to: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium (<em>Journal of NeuroEngineering and Rehabilitation</em>, (2023), 20, 1, (78), 10.1186/s12984-023-01198-5)

\ua9 The Author(s) 2024.Following publication of the original article [1], the author noticed the errors in Table 1, and in Discussion section. In Table 1 under Metric (Gait sequence detection) column, the algorithms GSDB was updated with wrong description, input, output, language and citation and GSDc with wrong description has been corrected as shown below: (Table presented.) Description of algorithms for each metric: gait sequence detection (GSD), initial contact event detection (ICD), cadence estimation (CAD) and stride length estimation (SL) Metric Name Description Input Output Language References GSDA Based on a frequency-based approach, this algorithm is implemented on the vertical and anterior–posterior acceleration signals. First, these are band pass filtered to keep frequencies between 0.5 and 3 Hz. Next, a convolution of a 2 Hz sinewave (representing a template for a gait cycle) is performed, from which local maxima will be detected to define the regions of gait acc_v: vertical acceleration acc_ap: anterior–posterior acceleration WinS = 3 s; window size for convolution OL = 1.5 s; overlap of windows Activity_thresh = 0.01; Motion threshold Fs: sampling frequency Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Iluz, Gazit [40] GSDB This algorithm, based on a time domain-approach, detects the gait periods based on identified steps. First, the norm of triaxial acceleration signal is low-pass filtered (FIR, fc = 3.2 Hz), then a peak detection procedure using a threshold of 0.1 [g] is applied to identify steps. Consecutive steps, detected using an adaptive step duration threshold are associated to gait sequences acc_norm: norm of the 3D-accelerometer signal Fs: sampling frequency th: peak detection threshold: 0.1 (g) Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Paraschiv-Ionescu, Newman [41] GSDc This algorithm utilizes the same approach as GSDBthe only difference being a different threshold for peak detection of 0.15 [g] acc_norm: norm of the 3D-accelerometer signal Fs: sampling frequency th: peak detection threshold: 0.15 (g) Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 7 N vector Matlab\uae Paraschiv-Ionescu, Newman [41] In Discussion section, the paragraph should read as "Based on our findings collectively, we recommend using GSDB on cohorts with slower gait speeds and substantial gait impairments (e.g., proximal femoral fracture). This may be because this algorithm is based on the acceleration norm (overall accelerometry signal rather than a specific axis/direction (e.g., vertical), hence it is more robust to sensor misalignments that are common in unsupervised real-life settings. Moreover, the use of adaptive threshold, that are derived from the features of a subject’s data and applied to step duration for detection of steps belonging to gait sequences, allows increased robustness of the algorithm to irregular and unstable gait patterns" instead of “Based on our findings collectively, we recommend using GSDB on cohorts with slower gait speeds and substantial gait impairments (e.g., proximal femoral fracture). This may be because this algorithm is based on the acceleration norm (overall accelerometry signal rather than a specific axis/direction (e.g., vertical), hence it is more robust to sensor misalignments that are common in unsupervised real-life settings [41]. Moreover, the use of adaptive thresholds, that are derived from the features of a subject’s data and applied to the amplitude of acceleration norm and to step duration for detection of steps belonging to gait sequences, allows increased robustness of the algorithm to irregular and unstable gait patterns”