Asthma is the most common chronic respiratory disease in the UK; however, the misdiagnosis rate is substantial. The lack of consistency in national guidelines and the paucity of data on the performance of diagnostic algorithms compound the challenges in asthma diagnosis. Asthma is a highly rhythmic disease, characterised by diurnal variability in clinical symptoms and pathogenesis. Asthma also varies day to day, seasonally and from year to year. As much as it is a hallmark for asthma, this variability also poses significant challenges to asthma diagnosis. Almost all established asthma diagnostic tools demonstrate diurnal variation, yet few are performed with standardised timing of measurements. The dichotomous interpretation of diagnostic outcomes using fixed cut-off values may further limit the accuracy of the tests, particularly when diurnal variability straddles cut-off values within a day, and careful interpretation beyond the ‘positive’ and ‘negative’ outcome is needed. The day-to-day and more long-term variations are less predictable and it is unclear whether performing asthma diagnostic tests during asymptomatic periods may influence diagnostic sensitivities. With the evolution of asthma diagnostic tools, home monitoring and digital apps, novel strategies are needed to bridge these gaps in knowledge, and circadian variability should be considered during the standardisation process. This review summarises the biological mechanisms of circadian rhythms in asthma and highlights novel data on the significance of time (the fourth dimension) in asthma diagnosis.

Background Current evidence suggests that in children there is a significant, albeit weak, association between asthma and obesity. Studies generally use body mass index (BMI) in evaluating body adiposity, but there are limitations to its use. Method Children from a population-based study attending follow-up (age 11 years) were weighed, measured and had percent body (PBF) and truncal (PTF) fat assessed using bioelectrical impedance. They were skin prick tested and completed spirometry. Parents completed a validated respiratory questionnaire. Children were defined as normal or overweight according to BMI and PBF cut-offs. We tested the association between these adiposity markers with wheeze, asthma, atopy, and lung-function. Results Six hundred forty-six children (339 male) completed follow-up. BMI z-score, PBF, and PTF were all positively associated with current wheeze (odds ratio [95% CI]: 1.27 [1.03, 1.57], P = 0.03; 1.05 [1.00, 1.09], P = 0.03; 1.04 [1.00, 1.08], P = 0.04, respectively). Similar trends were seen with asthma. However, when examining girls and boys separately, significant positive associations were found with PBF and PTF and asthma but only in girls (gender interaction P = 0.06 and 0.04, respectively). Associations between being overweight and wheezing and asthma were stronger when overweight was defined by PBF (P = 0.007, 0.03) than BMI (P > 0.05). Higher BMI was significantly associated with an increase in FEV₁ and FVC, but only in girls. Conversely, increasing body fat (PBF and PTF) was associated with reduced FEV₁ and FVC, but only in boys. No associations between adiposity and atopy were found. Conclusion All adiposity measures were associated with wheeze, asthma, and lung function. However, BMI and PBF did not have the same effects and girls and boys appear to be affected differently. Pediatr Pulmonol. 2014; 49:1049–1057. © 2013 The Authors. Pediatric Pulmonology published by Wiley Periodicals, Inc.

The exhaled breath represents an ideal matrix for noninvasive biomarker discovery, and exhaled metabolomics have the potential to be clinically useful in the era of precision medicine. In this concise translational review, we specifically address volatile organic compounds in the breath, with a view toward fulfilling the promise of these as actionable biomarkers, in particular, for lung diseases. We review the literature paying attention to seminal work linked to key milestones in breath research; discuss potential applications for breath biomarkers across disease areas and healthcare systems, including the perspectives of industry; and outline critical aspects of study design that will need to be considered for any pivotal research going forward if breath analysis is to provide robust validated biomarkers that meet the requirements for future clinical implementation.

Introduction At the end of the first year of the COVID-19 pandemic, more than 78 million known survivors were recorded. The long-term pulmonary sequelae of COVID-19 remain unknown. Methods We performed a retrospective analysis of a post-COVID follow-up service to estimate the burden of persistent pulmonary morbidity in hospitalised COVID survivors. Results A total of 221 patients were followed-up: 44 intensive care unit (ICU) and 177 ward patients. Further investigations were planned as per British Thoracic Society Guidelines: For all ICU patients (n = 44) and for 38 of 177 (21%) ward-based patients who had persistent symptoms and/or persistent radiographic changes on CXR at their initial 8-week follow-up visit. In the ward-based cohort, statistically significant associations with persistent symptoms were being an ex- or current smoker, having pre-existing diabetes, and having a longer length of stay. In patients requiring further investigations, pulmonary function tests (PFTs; n = 67) at an average of 15 weeks post-discharge showed abnormalities in at least one PFT parameter in 79% (equating to 24% of the entire cohort). The most common abnormality was an abnormal diffusion capacity of carbon monoxide (TLCO), highest in the ICU cohort (64% ICU vs. 38% non-ICU). TLCO correlated negatively with length of stay and with maximum inspired FiO₂ in the patient group as a whole. In ICU patients, TLCO correlated negatively with maximum inspired positive airway pressure. Computed tomography scans (n = 72) at an average of 18 weeks post-discharge showed evidence of persistent ground glass opacities in 44% and fibrosis in 21% (equating to 7% of the entire cohort). Conclusion Our data add to the growing evidence that there will be pulmonary sequelae in a proportion of COVID survivors, providing some insight into what may become a significant chronic global health problem.

Considerable variability exists between asthma diagnostic guidelines. We tested the performance characteristics of the European Respiratory Society (ERS), the National Institute for Health and Care Excellence (NICE) and the Global Initiative for Asthma (GINA) guidelines for the diagnosis of asthma in adults. In this prospective observational study (ISRCTN-11676160, May 2019-June 2022), participants referred from primary care with clinician-suspected asthma underwent comprehensive investigation including: spirometry, bronchodilator reversibility, fractional exhaled nitric oxide, peak expiratory flow variability, bronchial challenge testing with methacholine and mannitol, and responsiveness to inhaled corticosteroid therapy. Results were reviewed by a panel of asthma specialists to determine asthma diagnosis (reference standard) and compared to each diagnostic test and the ERS, NICE and GINA diagnostic algorithms (index tests). The sensitivity, specificity, positive predictive and negative predictive values were calculated. One hundred and forty adults were enrolled and 118 given a definitive diagnostic outcome [75 female; mean (SD) age 36 (12) years; 70 (59%) with asthma] and included in the analysis. Sensitivity of individual tests was poor (15-62%), but they provided good specificity at the most stringent thresholds (range: 88-100%). The sensitivity/specificity of ERS, NICE and GINA was 81/85%, 41/100% and 47/100%, respectively. Concordance between guidelines was only moderate (Cohen's Kappa 0.45-0.51). Current guidelines for the diagnosis of asthma in adults provide either excellent specificity but low sensitivity (GINA and NICE) or only reasonable sensitivity and specificity (ERS). All guidelines therefore have limitations with regards to their clinical application; new guidelines are needed but should be tested prospectively before roll out. This work was supported by the Manchester NIHR Biomedical Research Centre (BRC) (grant no. BRC-1215-20007, and NIHR203308), Asthma UK/Innovate (grant no. AUK-PG-2018-406), GSK ID 212474 and North West Lung Centre Charity.