We thank the authors for their letter in response to our paper(1). We disagree however that the data among the tested subgroup are more informative than our other findings. This is because the small subgroup (1.1% of app users - 0.7% negative, 0.3% positive, 0.1% result unknown) who reported that they had undergone testing for COVID-19 at this relatively early stage in the pandemic (the month from 24th March 2020) were heavily selected. Testing policies focused on healthcare workers and others interacting with healthcare - in particular, patients tested who may have been attending healthcare settings for other, non-COVID-19 related, conditions. As numerous health conditions are smoking-related this would tend to increase the exposure of smokers without COVID-19 to testing. For these reasons, as discussed in the paper, the finding that smoking rates were lower in those testing positive is likely to be due to sampling bias. Rather than being “more relevant”, extrapolation from this subgroup to population risk is entirely inappropriate.
The letter does appear to misunderstand the groups presented – the “standard user” group were not asymptomatic during the study. Rather, as set out in the first paragraph of the results, they were individuals who at the point of registration with the Zoe COVID Symptom Study App did not think that they already had COVID-19. Among this group of “standard users” current smokers were more likely to report the onset of new symptoms suggesting...
We thank the authors for their letter in response to our paper(1). We disagree however that the data among the tested subgroup are more informative than our other findings. This is because the small subgroup (1.1% of app users - 0.7% negative, 0.3% positive, 0.1% result unknown) who reported that they had undergone testing for COVID-19 at this relatively early stage in the pandemic (the month from 24th March 2020) were heavily selected. Testing policies focused on healthcare workers and others interacting with healthcare - in particular, patients tested who may have been attending healthcare settings for other, non-COVID-19 related, conditions. As numerous health conditions are smoking-related this would tend to increase the exposure of smokers without COVID-19 to testing. For these reasons, as discussed in the paper, the finding that smoking rates were lower in those testing positive is likely to be due to sampling bias. Rather than being “more relevant”, extrapolation from this subgroup to population risk is entirely inappropriate.
The letter does appear to misunderstand the groups presented – the “standard user” group were not asymptomatic during the study. Rather, as set out in the first paragraph of the results, they were individuals who at the point of registration with the Zoe COVID Symptom Study App did not think that they already had COVID-19. Among this group of “standard users” current smokers were more likely to report the onset of new symptoms suggesting a diagnosis of COVID-19. For presenting with “classic” COVID-19 symptoms (cough, fever, breathlessness) the adjusted OR[95%CI] was 1.14[1.10 to 1.18]; for presenting >5 symptoms 1.29[1.26 to 1.31]; for >10 symptoms 1.50[1.42 to 1.58].
Among the 157,000 people who did think that they already had COVID-19 at the time of registration, 13.79% were current smokers compared to 10.96% among all users, further supporting increased susceptibility from tobacco use.
As we set out in the paper, the pattern of association between reported symptoms did not vary between smokers and non-smokers and therefore cannot be explained by the presence of specific symptoms related to smoking.
It is also worth highlighting those reporting that they had tested positive for SARS-CoV-2, a higher symptom burden was reported among smokers and smokers were also twice as likely to have needed to attend hospital because of COVID-19 (OR 2.11 [1.41 to 3.11]) compared to non-smokers, a further marker of increased susceptibility.
Our results are also consistent with the OPENSAFELY study based on data from the primary care records of 17.3 million adults in the UK which found that, adjusted for age and sex, current smoking was associated with a hazard ratio for COVID-19-related death of 1.14 (1.05–1.23)(2).
The tobacco industry kills an estimated 8.7 million people per year globally and places substantial burdens on livelihoods and ecosystems, particularly in low and middle income countries.(3, 4) We believe that our data do show that smokers are at an increased risk from COVID-19, but regardless of any direct link, steps to bring smoking to an end must continue,(5, 6) both because of the enormous broader health harms and because of the effect that acute smoking-related illness has on health system capacity and resilience.
REFERENCES
1. Hopkinson NS, Rossi N, El-Sayed Moustafa J, Laverty AA, Quint JK, Freidin M, et al. Current smoking and COVID-19 risk: results from a population symptom app in over 2.4 million people. Thorax. 2021.
2. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430-6.
3. Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 2020;396(10258):1223-49.
4. Zafeiridou M, Hopkinson NS, Voulvoulis N. Cigarette Smoking: An Assessment of Tobacco’s Global Environmental Footprint Across Its Entire Supply Chain. Environmental science & technology. 2018;52(15):8087-94.
5. Hopkinson NS. The path to a smoke-free England by 2030. BMJ (Clinical research ed). 2020;368:m518-m.
6. Britton J, Arnott D, McNeill A, Hopkinson N. Nicotine without smoke—putting electronic cigarettes in context. BMJ. 2016;353.
The idea that smoking might have a protective effect against COVID-19 is an intriguing, man bites dog type of story, which gives it a certain attraction. Happily, it appears to be false and the assumption of harm has turned out to be correct[1-5].
Our data show clearly that in the 2.4 million Zoe COVID Symptom Study App users, people who smoked were at increased risk of symptomatic COVID-19[2] and were at risk of more severe disease, which is consistent with a systematic review of patients hospitalized with COVID-19[4]. Our findings are also consistent with The UCL COVID-19 Social Study3 which found increased risk of test confirmed COVID-19 (OR=2.14 (1.49–3.08)) and with the COVIDENCE study where smokers had an OR of1.42 (0.99-2.05) for test-confirmed COVID-19[1].
The OpenSafely dataset based on data from the primary care records of 17.3 million adults in the UK found that, adjusted for age and sex, also identifies smoking as a risk factor - current smoking was associated with a hazard ratio for COVID-19-related death of 1.14 (1.05–1.23)5. The apparently protective effect in the “fully adjusted” model is due to over-correction producing collider bias.
Since any protective effect of smoking in COVID-19 appears to be illusory, pursuing a mechanism for it is unlikely to be productive.
References
1 Holt H, Talaei M, Greenig M, et al. Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK). medRxiv 2021:2021.2003...
The idea that smoking might have a protective effect against COVID-19 is an intriguing, man bites dog type of story, which gives it a certain attraction. Happily, it appears to be false and the assumption of harm has turned out to be correct[1-5].
Our data show clearly that in the 2.4 million Zoe COVID Symptom Study App users, people who smoked were at increased risk of symptomatic COVID-19[2] and were at risk of more severe disease, which is consistent with a systematic review of patients hospitalized with COVID-19[4]. Our findings are also consistent with The UCL COVID-19 Social Study3 which found increased risk of test confirmed COVID-19 (OR=2.14 (1.49–3.08)) and with the COVIDENCE study where smokers had an OR of1.42 (0.99-2.05) for test-confirmed COVID-19[1].
The OpenSafely dataset based on data from the primary care records of 17.3 million adults in the UK found that, adjusted for age and sex, also identifies smoking as a risk factor - current smoking was associated with a hazard ratio for COVID-19-related death of 1.14 (1.05–1.23)5. The apparently protective effect in the “fully adjusted” model is due to over-correction producing collider bias.
Since any protective effect of smoking in COVID-19 appears to be illusory, pursuing a mechanism for it is unlikely to be productive.
References
1 Holt H, Talaei M, Greenig M, et al. Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK). medRxiv 2021:2021.2003.2027.21254452
2 Hopkinson NS, Rossi N, El-Sayed Moustafa J, et al. Current smoking and COVID-19 risk: results from a population symptom app in over 2.4 million people. Thorax 2021 https://pubmed.ncbi.nlm.nih.gov/33402392/
3 Jackson SE, Brown J, Shahab L, et al. COVID-19, smoking and inequalities: a study of 53 002 adults in the UK. Tobacco Control 2020:tobaccocontrol-2020-055933
4 Reddy RK, Charles WN, Sklavounos A, et al. The effect of smoking on COVID-19 severity: A systematic review and meta-analysis. Journal of medical virology 2021; 93:1045-1056
5 Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020; 584:430-436
There is no question that the harms of smoking hugely outweigh any potential health benefits. Many people, ourselves included, assumed at the beginning of the pandemic that greater susceptibility to COVID-19 would be another harm of tobacco smoking to be added to the long list. Surprisingly, most of the epidemiological data published over the last year do not support this claim. Indeed whereas ex-smokers are consistently found to be at increased risk of both SARS-CoV-2 infection and severe COVID-19, current smokers are consistently at lower risk than ex-smokers and in many studies they appear to be at a lower risk than never smokers. The lower infection rate in smokers compared to non-smokers and ex-smokers has been found across 62 studies (1, 2), including now a full cohort with a dose-response pattern (3).
The authors’ response does not counter the observation that among nearly 27,000 individuals who had a SARS-CoV-2 test in their study, smoking prevalence was lower in those who tested positive than in those who tested negative.
In the OpenSAFELY study (4) too, the direction of the association between smoking and death from COVID-19 depends critically on what adjustments are made. The primary analysis appears to be based on a fully adjusted Cox regression model in which the hazard ratio for current smokers relative to never smokers was 0.89 (95% CI 0.82-0.97). The value (1.14; 1.05-1.23) cited by Hopkinson and colleagues is after adjusting for age and sex...
There is no question that the harms of smoking hugely outweigh any potential health benefits. Many people, ourselves included, assumed at the beginning of the pandemic that greater susceptibility to COVID-19 would be another harm of tobacco smoking to be added to the long list. Surprisingly, most of the epidemiological data published over the last year do not support this claim. Indeed whereas ex-smokers are consistently found to be at increased risk of both SARS-CoV-2 infection and severe COVID-19, current smokers are consistently at lower risk than ex-smokers and in many studies they appear to be at a lower risk than never smokers. The lower infection rate in smokers compared to non-smokers and ex-smokers has been found across 62 studies (1, 2), including now a full cohort with a dose-response pattern (3).
The authors’ response does not counter the observation that among nearly 27,000 individuals who had a SARS-CoV-2 test in their study, smoking prevalence was lower in those who tested positive than in those who tested negative.
In the OpenSAFELY study (4) too, the direction of the association between smoking and death from COVID-19 depends critically on what adjustments are made. The primary analysis appears to be based on a fully adjusted Cox regression model in which the hazard ratio for current smokers relative to never smokers was 0.89 (95% CI 0.82-0.97). The value (1.14; 1.05-1.23) cited by Hopkinson and colleagues is after adjusting for age and sex only. Interestingly if one looks at the hazard of COVID-19 related death in current smokers relative to ex-smokers it is 0.75 and 0.80 in the fully-adjusted and the age-and-sex adjusted analyses respectively (and both are significantly less than 1.00 because the confidence intervals for former and current smokers don’t overlap).
Hopkinson et al. argue that the finding of lower smoking rates among positive cases was due to over-testing smokers attending healthcare facilities for other reasons. As we pointed out in our letter, smoking prevalence in the tested group was lower than in those not tested, which reduces the likelihood of such confounding. This does not rule out a possibility of confounding altogether, but we think the risk of confounding is much greater in the approach that the authors used, i.e. focusing on symptoms rather than on actual infections. Smokers are more likely to have symptoms and so they will obviously be over-represented in such samples.
If smoking reduces the risk of COVID-19 infection, it would of course not lead to encouraging smoking, but to identifying the active ingredient of the effect and the development of new preventive measures that could be of global importance.
1. Simons, D., Shahab, L., Brown, J., and Perski, O. (2020) The association of smoking status with SARS‐CoV‐2 infection, hospitalization and mortality from COVID‐19: a living rapid evidence review with Bayesian meta‐analyses (version 7). Addiction, https://doi.org/10.1111/add.15276.
2. Simons D, Shahab L, Brown J, Perski O. The association of smoking status with SARS-CoV-2 infection, hospitalisation and mortality from COVID-19: A living rapid evidence review with Bayesian meta-analyses (version 11, 2. March 2021) Qeios ID: UJR2AW.13; https://doi.org/10.32388/UJR2AW.13
3. Paleiron N, Mayet A, Marbac V, et al. Impact of Tobacco Smoking on the risk of COVID-19.A large scale retrospective cohort study [published online ahead of print, 2021 Jan 9]. Nicotine Tob Res. 2021;ntab004. doi:10.1093/ntr/ntab004
4. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430-6.
We read the interesting report by Mason et al, “Respiratory exacerbations are associated with muscle loss in current and former smokers”.[1] In this study, the authors demonstrated that exacerbations are associated with accelerated loss of pectoralis muscles (PMs) in two large observational cohorts and quantified the impact of each annual exacerbation as the equivalent of 6 months of age-expected decline.
Skeletal muscle loss is one of the major systemic manifestations associated with mortality in patients with COPD. Not only systemic muscle loss but also loss of specific muscle groups are associated with clinical outcomes such as exacerbations and mortality in patients with COPD.[2, 3] Moreover, muscle loss can occur heterogeneously.[4] This may be partially because each muscle group has its physiological function or biological characteristics such as muscle fiber composition. This supports that loss of specific muscle groups may have different implications in the clinical course of COPD.
We previously analyzed the cross-sectional area of erector spinae muscles (ESMCSA) and that of PMs (PMCSA) in male patients with COPD using chest CT.[3] ESMs are ones of antigravity muscles which are involved in maintaining an upright posture. PMs play an important role in the movement of upper limbs. Both muscles also act as accessory inspiratory muscles. ESMs are composed of 60% type 1 fibers and 40% of type 2 fibers and PMs are composed in the reverse...
We read the interesting report by Mason et al, “Respiratory exacerbations are associated with muscle loss in current and former smokers”.[1] In this study, the authors demonstrated that exacerbations are associated with accelerated loss of pectoralis muscles (PMs) in two large observational cohorts and quantified the impact of each annual exacerbation as the equivalent of 6 months of age-expected decline.
Skeletal muscle loss is one of the major systemic manifestations associated with mortality in patients with COPD. Not only systemic muscle loss but also loss of specific muscle groups are associated with clinical outcomes such as exacerbations and mortality in patients with COPD.[2, 3] Moreover, muscle loss can occur heterogeneously.[4] This may be partially because each muscle group has its physiological function or biological characteristics such as muscle fiber composition. This supports that loss of specific muscle groups may have different implications in the clinical course of COPD.
We previously analyzed the cross-sectional area of erector spinae muscles (ESMCSA) and that of PMs (PMCSA) in male patients with COPD using chest CT.[3] ESMs are ones of antigravity muscles which are involved in maintaining an upright posture. PMs play an important role in the movement of upper limbs. Both muscles also act as accessory inspiratory muscles. ESMs are composed of 60% type 1 fibers and 40% of type 2 fibers and PMs are composed in the reversed ratio.[5] Both ESMCSA and PMCSA were significantly associated with age, body mass index (BMI), and disease severity in our study.[3] However, compared to PMCSA, ESMCSA showed a better association with physical activity and a significant association with mortality of patients with COPD.[3, 6] And in a subsequent longitudinal analysis of ESMCSA, we demonstrated that both ESMCSA at baseline and accelerated decline in ESMCSA, which was defined as over 10% decline in three years of interval, were independently associated with all-cause mortality. And we also demonstrated that accelerated decline of ESMCSA was significantly associated with frequent exacerbations and the percentage of the wall area of bronchi (WA%).[7]
These findings raise the question of whether different clinical factors are associated with a decrease in ESMCSA and PMCSA, respectively. To address this, we performed a post-hoc analysis of our prospective cohort study,[3, 7] and 102 male patients with COPD who undertook chest CT with three years of the interval were analyzed (Age; 71.3±8.3 years, GOLD stage; I/II/III/IV 20/47/28/7). Both ESMCSA and PMCSA significantly decreased in three years of interval (ESMCSA; 30.52±6.93 cm2 at baseline vs. 28.92±6.84 cm2 at follow-up, p<0.0001, PMCSA; 26.51±7.36 cm2 vs. 25.79±7.62 cm2, p=0.035). And the percentage of decrease in ESMCSA (([ESMCSA at baseline] – [ESMCSA at follow-up]) / [ESMCSA at baseline], %ΔESM) and that in PMCSA (%ΔPM) were significantly but mildly correlated (r=0.25, p=0.0097), which supports heterogeneous nature of muscle loss in patients with COPD. Concerning this, it is quite curious whether correlations between ESMCSA decline and PMCSA decline in their two large cohort studies were also heterogeneous.
Next, we compared clinical parameters associated with accelerated muscle loss using the cut-off value of double the mean value of %ΔESM (10%) or %ΔPM (5%). Patients with accelerated decline in PMCSA showed significantly greater decrease in BMI ([BMI at baseline] – [BMI at follow-up], ΔBMI) (p=0.0002), lower Charlson index (p=0.031) and higher WA% (p=0.040). Multivariate logistic regression analysis revealed that accelerated decline in ESMCSA was associated with WA% (Odds Ratio (OR) 1.39 [95% confidence interval (CI); 1.11-1.80], p=0.0039) and the number of moderate-to-severe exacerbations during follow-up (OR 1.37 [1.03-1.91], p=0.032).[7] On the other hand, accelerated decline in PMCSA was associated only with ΔBMI (OR 2.12 [1.28-3.53], p=0.0038).
The present analysis confirmed that muscle loss can occur heterogeneously and may reflect different aspects of patients with COPD. ESMs rather than PMs were more susceptible to decrease by frequent exacerbations. On the contrary, accelerated loss of PMs was significantly associated with accelerated weight loss.
As shown in Mason’s article, exacerbations of COPD cause skeletal muscle loss, however, following physical inactivity also affects skeletal muscle loss.[8] From this perspective, antigravity muscle which reflects physical activity will decrease more compared to other types of muscles.[3, 4] We can speculate that frequent exacerbations may contribute to accelerated loss of ESMs via physical inactivity. Accelerated loss of PMs, consist of a higher percentage of type 2 muscle fiber, may decrease due to exacerbations but it could be an indirect phenomenon. This may be partially because type 2 muscle fibers are more vulnerable to fasting than type 1 muscle fibers.[9]
Unlike Mason’s study, frequent exacerbations were not associated with accelerated decline in PMCSA in our analysis. This discrepancy was probably due to the small cohort. And the absolute value of PMCSA in the COPDGene or ECLIPSE cohort was higher than that of ours,[1, 3] which may be partially because patients with younger age and higher BMI were enrolled in those large cohorts. These differences in characteristics between our cohort and those cohorts might affect the results. Further analysis in those large cohorts would be expected to elucidate whether a rapid decline in PMCSA would be associated with mortality and whether ESMs would be more susceptible to frequent exacerbations than PMs. To be honest, we previously confirmed the validity of reported predictors of mortality,[3] which supports the validity and generalizability of the findings in our cohort. And an accelerated decline in ESMCSA demonstrated a significant association with not only exacerbations but also mortality even in our relatively small cohort. A decrease in ESMCSA is also shown to be significant for mortality in idiopathic pulmonary fibrosis.[10] Taken together, the clinical significance of evaluating ESMCSA would be suggested.
In conclusion, the present post-hoc analysis revealed that frequent exacerbations of COPD can contribute to accelerated loss of ESMs rather than PMs and it may come from physical inactivity. And it was also suggested that accelerated loss of PMs was significantly associated with nutrition status rather than frequent exacerbations. In a longitudinal analysis of skeletal muscle, it would be useful to analyze specific muscle groups according to physiological function and biological characteristics.
References
1. Mason SE, Moreta-Martinez R, Labaki WW, et al. Respiratory exacerbations are associated with muscle loss in current and former smokers. Thorax. 2021.
2. Guerri R, Gayete A, Balcells E, et al. Mass of intercostal muscles associates with risk of multiple exacerbations in COPD. Respir Med. 2010;104(3):378-88.
3. Tanimura K, Sato S, Fuseya Y, et al. Quantitative Assessment of Erector Spinae Muscles in Patients with Chronic Obstructive Pulmonary Disease. Novel Chest Computed Tomography-derived Index for Prognosis. Ann Am Thorac Soc. 2016;13(3):334-41.
4. Ikezoe T, Mori N, Nakamura M, et al. Effects of age and inactivity due to prolonged bed rest on atrophy of trunk muscles. Eur J Appl Physiol. 2012;112(1):43-8.
5. Johnson MA, Polgar J, Weightman D, et al. Data on the distribution of fibre types in thirty-six human muscles. An autopsy study. J Neurol Sci. 1973;18(1):111-29.
6. Tanabe N, Sato S, Tanimura K, et al. Associations of CT evaluations of antigravity muscles, emphysema and airway disease with longitudinal outcomes in patients with COPD. Thorax. 2021;76(3):295-7.
7. Tanimura K, Sato S, Sato A, et al. Accelerated Loss of Antigravity Muscles Is Associated with Mortality in Patients with COPD. Respiration. 2020;99(4):298-306.
8. Pitta F, Troosters T, Probst VS, et al. Physical activity and hospitalization for exacerbation of COPD. Chest. 2006;129(3):536-44.
9. Wang Y, Pessin JE. Mechanisms for fiber-type specificity of skeletal muscle atrophy. Curr Opin Clin Nutr Metab Care. 2013;16(3):243-50.
10. Nakano A, Ohkubo H, Taniguchi H, et al. Early decrease in erector spinae muscle area and future risk of mortality in idiopathic pulmonary fibrosis. Sci Rep. 2020;10(1):2312.
Statement of Ethics
The Ethics Committee of Kyoto University approved this study (approval No. E182), and all subjects provided written informed consent prior to participating.
With great interest we have read the study by Deshayes et al. The authors present two cases of silvernitrate (AgNO3) aspiration in laryngectomized patients.
1) In both cases the applicator tip broke off.
2) The authors conclude that treatment should comprise oral antibiotics and one should refrain from bronchial washing with sodium chloride solution.
With this response we would like to reply to both points addressed above.
1.
Five years ago we were confronted with the aspiration of an AgNO3 applicator tip in a laryngectomized patient. After the incident we analyzed the case to prevent future AgNO3 applicator tip aspiration. The AgNO3 pencil, used in both our case and the cases in the current article, is specifically designed to treat dermal lesions like verruca which requires repeated use. The pencil therefore contains a relatively large volume of AgNO3. AgNO3 is a brittle substance. When the pencil is used with a little too much pressure there is risk for the tip to break, and when used in a tracheostomy, there is risk for aspiration.
Our case led us to immediately stop using the AgNO3 pencils for treatment of granulation tissue in a tracheostomy. We strongly recommend the use of disposable AgNO3 cutaneous sticks for the treatment of granulation tissue around a tracheostomy. The disposable sticks contains less volume of AgNO3. Moreover, the stick is easier to use in narrow spaces like a tracheostomy.
2.
Aspiration of AgNO3 i...
With great interest we have read the study by Deshayes et al. The authors present two cases of silvernitrate (AgNO3) aspiration in laryngectomized patients.
1) In both cases the applicator tip broke off.
2) The authors conclude that treatment should comprise oral antibiotics and one should refrain from bronchial washing with sodium chloride solution.
With this response we would like to reply to both points addressed above.
1.
Five years ago we were confronted with the aspiration of an AgNO3 applicator tip in a laryngectomized patient. After the incident we analyzed the case to prevent future AgNO3 applicator tip aspiration. The AgNO3 pencil, used in both our case and the cases in the current article, is specifically designed to treat dermal lesions like verruca which requires repeated use. The pencil therefore contains a relatively large volume of AgNO3. AgNO3 is a brittle substance. When the pencil is used with a little too much pressure there is risk for the tip to break, and when used in a tracheostomy, there is risk for aspiration.
Our case led us to immediately stop using the AgNO3 pencils for treatment of granulation tissue in a tracheostomy. We strongly recommend the use of disposable AgNO3 cutaneous sticks for the treatment of granulation tissue around a tracheostomy. The disposable sticks contains less volume of AgNO3. Moreover, the stick is easier to use in narrow spaces like a tracheostomy.
2.
Aspiration of AgNO3 is rare, so there is no clinical evidence that proves for or against bronchial washing with sodium chloride solution. From a theoretical perspective there is an argument to advise bronchial washing with sodium chloride solution after AgNO3 aspiration. As previously concluded, AgNO3 is activated when it is dissolved in water. AgNO3 is neutralized when in contact with sodium chloride solution (AgNO3 → AgCl + NaNO3). Correspondingly, the manufacturer advices to repeatedly wash the stomach with sodium chloride solution after AgNO3 ingestion. The authors conclude that one should not wash with sodium chloride solution due to the risk for spread of AgNO3 and potential secondary stenosis. Corresponding to the manufacturer information AgNO3 is neutralized when dissolved in sodium chloride solution. Based on theory and manufacturer information we advise thorough bronchial washing with sodium chloride solution after AgNO3 aspiration.
We thank Tanimura and colleagues for their thoughtful commentary on our recent manuscript, “Respiratory exacerbations are associated with muscle loss in current and former smokers” and read their analysis of erector spinae muscle area (ESMA) with interest (1). In their commentary, they note that muscle loss can occur heterogeneously, with the greatest expected impact on the muscles of ambulation. They suggest that erector spinae muscles, due to their fiber composition and anti-gravity role, are a better reflection of inactivity-related muscle loss and posit that changes in pectoralis muscle area (PMA) may only reflect changes in nutrition (as measured by body mass index, BMI).
We agree that muscle loss is unlikely to be uniform; however, a disconnect has been reported between the postural muscles of the trunk and ambulatory muscle (e.g. quadriceps) weakness, despite similar fiber types (2). Few studies measure both groups of muscles simultaneously, but there is evidence that inspiratory force is more affected than peripheral muscle force in patients with COPD; implying that deconditioning is not the sole driver of muscle dysfunction (3). While the pectoralis muscle potentially underestimates inactivity-related atrophy, these studies suggest its role as an accessory muscle of inspiration makes it a reasonable target for capturing any underlying systemic process.
In contrast to Tanimura et al’s findings, in the COPDGene participants (n=8,603) BMI was more stro...
We thank Tanimura and colleagues for their thoughtful commentary on our recent manuscript, “Respiratory exacerbations are associated with muscle loss in current and former smokers” and read their analysis of erector spinae muscle area (ESMA) with interest (1). In their commentary, they note that muscle loss can occur heterogeneously, with the greatest expected impact on the muscles of ambulation. They suggest that erector spinae muscles, due to their fiber composition and anti-gravity role, are a better reflection of inactivity-related muscle loss and posit that changes in pectoralis muscle area (PMA) may only reflect changes in nutrition (as measured by body mass index, BMI).
We agree that muscle loss is unlikely to be uniform; however, a disconnect has been reported between the postural muscles of the trunk and ambulatory muscle (e.g. quadriceps) weakness, despite similar fiber types (2). Few studies measure both groups of muscles simultaneously, but there is evidence that inspiratory force is more affected than peripheral muscle force in patients with COPD; implying that deconditioning is not the sole driver of muscle dysfunction (3). While the pectoralis muscle potentially underestimates inactivity-related atrophy, these studies suggest its role as an accessory muscle of inspiration makes it a reasonable target for capturing any underlying systemic process.
In contrast to Tanimura et al’s findings, in the COPDGene participants (n=8,603) BMI was more strongly correlated with ESMA (r=0.42, p<0.001) than PMA (r=0.17, p< 0.001). Interestingly, both correlations were stronger in women compared to men. Because change in pectoral muscle area (PMA) is mildly correlated with change in BMI (r=0.20, p>0.001), our longitudinal analysis included BMI as a predictor.
Building on previously published work showing no association between ESMA and mortality in COPDGene participants without obstruction (4), we completed a sex-stratified analysis of n=8,603 participants over 12 years of follow-up using a Cox proportional hazards model controlling for age, race, smoking status, pack years, dyspnea score (MMRC), six-minute walk distance, FEV1 percent predicted, percent emphysema, and BMI. We found that the ESMA was not significantly associated with mortality (p=0.125 for men and p=0.157 for women). In an analogous model, PMA was associated with mortality; for each one cm2 decrease in PMA, men had a 1.1% (0.5-1.8%, p<0.001) increased risk of death and women had a 1.6% (0.3-2.9%, p=0.016) increased risk of death.
Differences in the cohort composition, measurement interval, and statistical methods in our work and that of Tanimura et. al. could be contributing to the discrepant results in our studies. For example, compared to their cohort, the men in COPDGene were substantially younger (59.6 ± 9.0 years), had higher BMI (28.4 ± 5.5 kg/m2), and had nearly twice the mean PMA (51.2 ± 15.6 cm2) and ESMA (58.7 ± 12.2 cm2). Additionally, we note that in COPDGene women had significantly lower ESMA (43.1 ± 9.4 cm2) and PMA (31.2 ± 8.3 cm2) and Black/African American participants had significantly higher ESMA (57.1 ± 13.9 cm2 vs 48.7 ± 12.5 cm2) and PMA (51.4 ± 18.4 cm2 vs 37.6 ± 12.9 cm2) compared to non-Hispanic Whites. The former finding is especially notable in the context of literature demonstrating differential muscle dysfunction in men and women with COPD (5, 6).
In conclusion, our analysis demonstrated that PMA is not only associated with exacerbations, but with mortality. We found significant differences in ESMA and PMA measurements across sex and race categories that may impact the generalizability of studies using cross-sectional muscle area as a proxy for fat free mass. Further research with broad, inclusive cohorts may help elucidate anthropometric differences in disease progression.
1. Mason SE, Moreta-Martinez R, Labaki WW, Strand M, Baraghoshi D, Regan EA, et al. Respiratory exacerbations are associated with muscle loss in current and former smokers. Thorax. 2021.
2. Man WD, Hopkinson NS, Harraf F, Nikoletou D, Polkey MI, Moxham J. Abdominal muscle and quadriceps strength in chronic obstructive pulmonary disease. Thorax. 2005;60(9):718-22.
3. Gosselink R, Troosters T, Decramer M. Distribution of muscle weakness in patients with stable chronic obstructive pulmonary disease. J Cardiopulm Rehabil. 2000;20(6):353-60.
4. Diaz AA, Martinez CH, Harmouche R, Young TP, McDonald ML, Ross JC, et al. Pectoralis muscle area and mortality in smokers without airflow obstruction. Respir Res. 2018;19(1):62.
5. Sharanya A, Ciano M, Withana S, Kemp PR, Polkey MI, Sathyapala SA. Sex differences in COPD-related quadriceps muscle dysfunction and fibre abnormalities. Chron Respir Dis. 2019;16:1479973119843650.
6. Ausin P, Martinez-Llorens J, Sabate-Bresco M, Casadevall C, Barreiro E, Gea J. Sex differences in function and structure of the quadriceps muscle in chronic obstructive pulmonary disease patients. Chron Respir Dis. 2017;14(2):127-39.
We thank Brennan et al, for sharing their experiences. In contrast to our observed reduction of more than 50% in AECOPD hospital admissions over a 6-month period, Brennan and colleagues observed a reduction of only 18% over a 4-month period. In addition, while we saw a significant and sustained decrease, Brennan et al. observed a decrease only in the first month following lockdown. At the fundamental level, respiratory viruses can spread either via contact, droplet or aerosols[1] and thus in theory mask wearing, social distancing and increased personal respiratory etiquette and community hygiene would reduce transmission and contribute to reduced incidence of AECOPD. The use of masks has been shown to reduce exposure to acute respiratory viruses by 46%[2].
We hypothesise that these differences could potentially be due to variations in the degree of adherence to mask wearing/social distancing, as well as nuances in public health measures introduced in various countries during the COVID-19 pandemic.
For instance, Singapore had mandated face-mask wearing in April 2020. The observations reported by Brennan et al terminated in June 2020 while Ireland only mandated face-mask wearing in August 2020. and hence may not have captured the impact of compulsory mask wearing. The difference in timing of implementation and enforcement of government policies during the COVID-19 pandemic possibly contributed to a different experience in Ireland.
We thank Brennan et al, for sharing their experiences. In contrast to our observed reduction of more than 50% in AECOPD hospital admissions over a 6-month period, Brennan and colleagues observed a reduction of only 18% over a 4-month period. In addition, while we saw a significant and sustained decrease, Brennan et al. observed a decrease only in the first month following lockdown. At the fundamental level, respiratory viruses can spread either via contact, droplet or aerosols[1] and thus in theory mask wearing, social distancing and increased personal respiratory etiquette and community hygiene would reduce transmission and contribute to reduced incidence of AECOPD. The use of masks has been shown to reduce exposure to acute respiratory viruses by 46%[2].
We hypothesise that these differences could potentially be due to variations in the degree of adherence to mask wearing/social distancing, as well as nuances in public health measures introduced in various countries during the COVID-19 pandemic.
For instance, Singapore had mandated face-mask wearing in April 2020. The observations reported by Brennan et al terminated in June 2020 while Ireland only mandated face-mask wearing in August 2020. and hence may not have captured the impact of compulsory mask wearing. The difference in timing of implementation and enforcement of government policies during the COVID-19 pandemic possibly contributed to a different experience in Ireland.
Aside from early implementation and mandatory public health measures, efforts from the government are also needed to maximise adoption of these public health measures. Egan et al conducted a study to evaluate the effect of infographics on public recall, sentiment and willingness to use face-masks during COVID-19[3]. The study showed that recall of the salient steps of effective mask wearing was significantly higher in participants who viewed the Singaporean Ministry of Health infographic. In addition, acknowledging the impact of pandemic public health measures on personal lives of the public, health messaging in Singapore has encouraged and emphasised social responsibility. These came in the form of financial aids to businesses and citizens and also fines for failure to comply to health policies[4]. Thus, while various countries may be implementing the same public health measures, enforcement and adherence by the public may differ. Further efforts by the government are essential to maximise adoption and these can vary between countries due to differences in political systems[5].
In Singapore, a recent survey showed that as of January 3 2021, 91% of Singaporean respondents stated compliance to face mask wearing in public places during the COVID-19 outbreak, up from 24% on Feb 21, 2020. In fact, by end April 2020, compliance rate was already 90%[6]. In Hong Kong, the compliance of face mask usage by HKSAR general public was 96.6% (range: 95.7% to 97.2%)[7]. Comparatively, in a large community survey conducted in Ireland, when self-reported compliance with health guidance (including hand hygiene, social distancing, mask wearing and other public health measures) was assessed on an 11-point score, the average score was only 7.44 (S.D 2.48)[8].
In conclusion, the different experience by Brennan and colleagues are interesting yet not unexpected. Enforcement and adherence to public health measures during a pandemic will vary from country to country. These are affected by the country’s organizational system as well as at an individual level – the public’s attitudes and socioeconomic behaviour during a pandemic which translate to adherence to strict public health measures.
References
1. Kutter JS, Spronken MI, Fraaij PL, et al. Transmission routes of respiratory viruses among humans. Curr Opin Virol 2018;28:142-51. doi: 10.1016/j.coviro.2018.01.001 [published Online First: 2018/02/18]
2. Jefferson T, Del Mar CB, Dooley L, et al. Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database of Systematic Reviews 2020(11) doi: 10.1002/14651858.CD006207.pub5
3. Egan M, Acharya A, Sounderajah V, et al. Evaluating the effect of infographics on public recall, sentiment and willingness to use face masks during the COVID-19 pandemic: a randomised internet-based questionnaire study. BMC Public Health 2021;21(1):367. doi: 10.1186/s12889-021-10356-0 [published Online First: 2021/02/19]
4. Chua AQ, Tan MMJ, Verma M, et al. Health system resilience in managing the COVID-19 pandemic: lessons from Singapore. BMJ Glob Health 2020;5(9) doi: 10.1136/bmjgh-2020-003317 [published Online First: 2020/09/18]
5. Abdullah WJ, Kim S. Singapore’s Responses to the COVID-19 Outbreak: A Critical Assessment. The American Review of Public Administration 2020;50(6-7):770-76. doi: 10.1177/0275074020942454
6. Hirschmann R. Share of people who wore masks in public COVID-19 pandemic in Singapore 2020-2021. January 2021. https://www.statista.com/statistics/1110983/singapore-wearing-masks-duri....
7. Cheng VC, Wong SC, Chuang VW, et al. The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. J Infect 2020;81(1):107-14. doi: 10.1016/j.jinf.2020.04.024 [published Online First: 2020/04/27]
8. Roozenbeek J, Schneider CR, Dryhurst S, et al. Susceptibility to misinformation about COVID-19 around the world. R Soc Open Sci 2020;7(10):201199. doi: 10.1098/rsos.201199 [published Online First: 2020/11/19]
We read with interest the recent study by our colleagues Tan et al (1) which reported the introduction of public health measures during the pandemic, such as social distancing and universal mask wearing, were observed to coincide with a marked reduction in transmission of other circulating respiratory viral infections. They reported a reduction in hospital admissions with acute exacerbation of COPD (AECOPD) by over 50% during the six month period of the pandemic from February to June 2020. They supported this observation with microbiological data showing a significant reduction in PCR-positive respiratory viral infections compared to the pre-pandemic era.
Ireland has the highest rate of hospitalisations for AECOPD in all OECD Countries (2). The first case of COVID-19 in the Republic of Ireland was reported on 29/02/2020 and stringent public health measures were introduced in mid-March to combat the spread (3).
We wish to describe our experiences of hospital admission with AECOPD during the first wave of the pandemic in a tertiary referral hospital in the West of Ireland. In our clinical practice, we noticed a reduction in patients admitted with COPD exacerbations at the beginning of the pandemic. We aimed to evaluate the impact of these infection control measures on our COPD population.
We conducted a retrospective cohort study of electronic health care records of patients who were hospitalised with a primary diagnosis of AECOPD over the four-month per...
We read with interest the recent study by our colleagues Tan et al (1) which reported the introduction of public health measures during the pandemic, such as social distancing and universal mask wearing, were observed to coincide with a marked reduction in transmission of other circulating respiratory viral infections. They reported a reduction in hospital admissions with acute exacerbation of COPD (AECOPD) by over 50% during the six month period of the pandemic from February to June 2020. They supported this observation with microbiological data showing a significant reduction in PCR-positive respiratory viral infections compared to the pre-pandemic era.
Ireland has the highest rate of hospitalisations for AECOPD in all OECD Countries (2). The first case of COVID-19 in the Republic of Ireland was reported on 29/02/2020 and stringent public health measures were introduced in mid-March to combat the spread (3).
We wish to describe our experiences of hospital admission with AECOPD during the first wave of the pandemic in a tertiary referral hospital in the West of Ireland. In our clinical practice, we noticed a reduction in patients admitted with COPD exacerbations at the beginning of the pandemic. We aimed to evaluate the impact of these infection control measures on our COPD population.
We conducted a retrospective cohort study of electronic health care records of patients who were hospitalised with a primary diagnosis of AECOPD over the four-month period of 01/03/2020 to 30/06/2020 which corresponded to the period where the strictest public health guidelines were in place. We compared this period to the same four-month period in the previous year as a control group. The primary outcome was the number of admissions with a primary diagnosis of AECOPD. The secondary outcomes we assessed were: a) the severity of AECOPD as determined by DECAF score (3) b) length of stay c) acute in-hospital mortality.
Overall, there were 123 hospitalisations with AECOPD in the 2020 cohort compared with 150 hospitalisations the previous year. This corresponds to a 18% reduction in hospital admissions. Table 1 and Table 2 below display the results. However, when subgroup analysis by month was performed, this reduction was primarily driven by a significant reduction in admissions during March 2020 (20 vs 42, p=0.02). There was no significant difference between the following three months between the pandemic and control period. Male patients accounted for just over 50% in both groups. Those in the pandemic group were significantly older, median 76 years (IQR 70-84) compared with 73.5 years (IQR 67-80), p<0.01.
We calculated DECAF scores (4) as a measure of the severity of exacerbations and allow an objective comparison between groups. There was no statistical or clinical difference in the mean DECAF scores between groups, 1.94 (+/- 1.34) and 1.73 (+/-1.25) nor the proportion of patients presenting with a DECAF of > 3 indicating a severe exacerbation. In the pandemic group 5.7% (n=7) died in hospital compared with 4.7% (n=7) in the control group. This was not significant and in-hospital mortality was slightly lower than other published studies on mortality in AECOPD (5,6) although there are many factors that can influence in-hospital mortality. There was no difference in median length of stay of those discharged, which was approximately 7 days in both groups. Interestingly, on subgroup analysis, more patients died during May 2020 than the previous May 2019 (10% (n=2) versus 0% (n=0), p <0.04) although the absolute numbers are very small.
Our experience with AECOPD during the pandemic period contrasts with the experiences of Tan et al, and indeed of a previous Hong Kong study which noted a 44% reduction in AECOPD during the pandemic period (7). While there was a reduction of 18% overall this is of smaller magnitude than the above studies of similar duration. Furthermore, the reduction is accounted for almost entirely by a dramatic reduction in admissions during the month of March alone which was the beginning of the lockdown period in Ireland, followed by a restoration of admissions to levels comparable to the previous year.
Although there were no detectable differences in DECAF scores of patients admitted, the overall trend of admissions along with the proportional increase in mortality during March suggests that hospital avoidance may account for the temporary but dramatic reduction in admissions. There have been well-publicised concerns regarding patients delaying seeking hospital care for emergencies such as myocardial infarctions and strokes, due to fears of contracting COVID-19 (8). Thus, it is plausible that patients with COPD who had mild to moderate exacerbations may have sought assistance from primary care facilities in the first instance resulting in a smaller but sicker cohort of patients presenting to hospital with a corresponding mortality bias.
We agree that reduced viral transmission is certainly an important factor in reducing AECOPD and the reports on reduction in influenza rates support Tan et al’s viral PCR studies (1,9). Anti-viral therapies merit an increased research focus to target therapeutic options. However, if viral reduction accounted in part for the reduction in AECOPD in our patient population, it appears to have been a transient phenomenon at most. Conversely and fortuitously, our patients appeared to have benefited from the extremely low levels of COVID-19 during the first wave of the pandemic in our predominantly rural catchment area. Only one patient tested positive for SARS-COV-2 during the study period and recovered fully.
In summary, we feel that our contrasting results to Tan et al are of interest and merit discussion. Our findings are plausible explained by differences in public support and adherence to universal mask wearing during the first wave of the pandemic and public attitudes to the risk of nosocomial transmission of COVID-19.
References
1. Tan JY, Conceicao EP, Wee LE, Sim XY, Venkatachalam I. COVID-19 public health measures: a reduction in hospital admissions for COPD exacerbations. Thorax. 2020 Dec 3.
2. National Clinical Effectiveness Committee. National Clinical Guideline: Management of Chronic Obstructive Pulmonary Disease (COPD) Version 5.0. Department of Health. July 2020
3. Department of Health. Statement from National Public Health Emergency Team- 29 February 2020. February 2020. Available at gov.ie - Statement from the National Public Health Emergency Team - Saturday 29 February (www.gov.ie)
4. Steer J, Gibson J, Bourke SC. The DECAF Score: predicting hospital mortality in exacerbations of chronic obstructive pulmonary disease. Thorax. 2012 Nov 1;67(11):970-6.
5. Connors Jr AF, Dawson NV, Thomas C, Harrell Jr FE, Desbiens N, Fulkerson WJ, Kussin P, Bellamy P, Goldman L, Knaus WA. Outcomes following acute exacerbation of severe chronic obstructive lung disease. The SUPPORT investigators (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments). American journal of respiratory and critical care medicine. 1996 Oct;154(4):959-67.
6. Gunen H, Hacievliyagil SS, Kosar F, Mutlu LC, Gulbas G, Pehlivan E, Sahin I, Kizkin O. Factors affecting survival of hospitalised patients with COPD. European Respiratory Journal. 2005 Aug 1;26(2):234-41.
7. Chan KP, Ma TF, Kwok WC et al. Significant reduction in hospital admissions for acute exacerbation of chronic obstructive pulmonary disease in Hong Kong during coronavirus disease 2019 pandemic. Respiratory Medicine. 2020 Jul 12:106085.
8. Lange SJ, Ritchey MD, Goodman AB et al. Potential indirect effects of the COVID-19 pandemic on use of emergency departments for acute life-threatening conditions—United States, January–May 2020. MMWR Morb Mortal Wkly Rep 2020 Jun 26;69(25):795.
9. Soo RJ, Chiew CJ, Ma S et al. Decreased influenza incidence under COVID-19 control measures, Singapore. Emerg Infect Dis. 2020 Aug;26(8):1933.
2020 2019 P value
No of AECOPD 123 150
Male-N (%) 66 (53.7%) 77 (51.3%) P=0.383
Age- Median 76 (70-84) 73.5 (67-80) P <0.01
March (N) 20 42 0.02
April (N) 38 40 0.44
May (N) 33 32 0.29
June (N) 32 36 0.70
Table 2. Clinical Presentation -DECAF score of patients
2020 (n=123) 2019 (n=150) P value
DECAF
Mean ±SD 1.94 (+/- 1.34) 1.73 (+/- 1.25) 0.182
DECAF ≥3: N (%) 32 (26%) 41 (27.3%) 0.81
Clinical Parameters of DECAF score N (%)
Dyspnoea mMRC 5a/5b 70 82 0.711
Dyspnoea mMRC 5a 46 68 0.187
Dyspnoea mMRC 5b 24 14 0.015
Eosinopenia 54 63 0.749
Consolidation 38 52 0.509
Atrial fibrillation 40 34 0.069
Acidaemia 13 16 0.97
Outcomes
LOS (days) Median 6.5 (4-11) 7 (4-11) 0.98
Deaths 7 (5.7%) 7 (4.7%) 0.704
Deaths March 2 (10%) 0 (0%) 0.037
Deaths April 1 (2.6%) 1 (2.5%) 0.97
Deaths May 2 (6%) 3 (9.4%) 0.62
Deaths June 2 (6.3%) 3 (8.3%) 0.74
The influence of obesity on both asthma and T2 biomarkers remains poorly understood and we fully agree this requires further investigation, as does the relationship between obesity, depression and persistent symptoms of breathlessness. However, the data correlating obesity and FeNO is conflicting and the reported weak positive associations have often not been adjusted for corticosteroid dose and may simply reflect higher doses of corticosteroid therapy in more breathless obese patients than by those of normal weight, rather than a specific mechanistic relationship.
Moreover, the UKSAR population appears very different from the cohorts described in some of these reports. For example, the average FeNO was only 25ppb in the Komakula study, whilst in the study by Lugogo subjects were predominantly T2-low across all BMI categories: the upper quartile value of blood eosinophils in both lean and obese groups was <300 cells/µL, whilst the upper quartile of FeNO in both lean and obese groups was <30ppb. In contrast, even in the UKSAR T2 high cohort, the mean BMI was in the obese range.
The nature and veracity of the ‘T2-low’ phenotype remains unclear, particularly in severe asthma. What is increasingly apparent is that patients are frequently prescribed high dose inhaled and systemic corticosteroids for respiratory symptoms, which suppresses T2 inflammation in the process. In the context of obesity and other co-morbidities known to be associated with increased re...
The influence of obesity on both asthma and T2 biomarkers remains poorly understood and we fully agree this requires further investigation, as does the relationship between obesity, depression and persistent symptoms of breathlessness. However, the data correlating obesity and FeNO is conflicting and the reported weak positive associations have often not been adjusted for corticosteroid dose and may simply reflect higher doses of corticosteroid therapy in more breathless obese patients than by those of normal weight, rather than a specific mechanistic relationship.
Moreover, the UKSAR population appears very different from the cohorts described in some of these reports. For example, the average FeNO was only 25ppb in the Komakula study, whilst in the study by Lugogo subjects were predominantly T2-low across all BMI categories: the upper quartile value of blood eosinophils in both lean and obese groups was <300 cells/µL, whilst the upper quartile of FeNO in both lean and obese groups was <30ppb. In contrast, even in the UKSAR T2 high cohort, the mean BMI was in the obese range.
The nature and veracity of the ‘T2-low’ phenotype remains unclear, particularly in severe asthma. What is increasingly apparent is that patients are frequently prescribed high dose inhaled and systemic corticosteroids for respiratory symptoms, which suppresses T2 inflammation in the process. In the context of obesity and other co-morbidities known to be associated with increased respiratory symptoms, such as breathing pattern disorders and laryngeal dysfunction, this leads to high levels of avoidable corticosteroid-induced toxicity. The recent study by Heaney and colleagues in the RASP-UK programme, demonstrated that when corticosteroids were reduced based on T2-biomarkers, the maximal prevalence of T2-Low severe asthma was ~5%. Importantly in the sub-group analysis of patients with “uncontrolled asthma” defined as ACQ-7≥1.5, biomarker directed care resulted in significant reduction of corticosteroid treatment with no loss of control and patients in this uncontrolled group were predominantly female, obese with restrictive lung function, more likely to be on oral corticosteroids and with higher levels of reflux, depression and osteoporosis, all consistent with corticosteroid overtreatment.
Until such time as corticosteroid doses are more effectively optimized in a T2 biomarker directed fashion, the influence of steroids themselves on many inflammatory pathways will continue to hamper our understanding of what mechanistically constitutes ‘T2-low’ asthma.
References
Komakula S, Khatri S, Mermis J, et al. Body mass index is associated with reduced exhaled nitric oxide and higher exhaled 8-isoprostanes in asthmatics. Respir Res. 2007Apr 16;8(1):32.
Lugogo N, Green CL, Agada N, et al. Obesity's effect on asthma extends to diagnostic criteria. J Allergy Clin Immunol. 2018;141(3):1096-1104.
Heaney LG, Busby J, Hanratty CE, et al. Composite type-2 biomarker strategy versus a symptom-risk-based algorithm to adjust corticosteroid dose in patients with severe asthma: a multicentre, single-blind, parallel group, randomised controlled trial. Lancet Respir Med. 2021 Jan;9(1):57-68.
We read with interest the recent paper from DJ Jackson et al, “Characterisation of patients with severe asthma in the UK Severe Asthma Registry in the biologic era” [1], and share their concerns regarding the risk of excessive corticosteroid exposure in T2-low individuals. We congratulate the authors for gathering such an extensive range of data in this large cohort of people with severe asthma, enabling meaningful comparisons, particularly between biologic and non-biologic populations. We echo the call for further work to identify and validate pragmatic T2-low endotype-specific biomarkers through clearer understanding of this inflammatory cascade. This cohort of patients continues to be under-served, made all the more evident by the paucity of novel therapies in this era of precision medicine.
We note the authors’ comments on T2-biomarker increase with corticosteroid dose reduction, and the presence of a historic T2-high profile in some individuals from the T2-low group. Whilst the postulated explanation reported by the authors, one of corticosteroid-induced T2-biomarker suppression, is undoubtedly a key factor (and indeed supported by the significant difference in corticosteroids between the groups), we would suggest another important factor that may be relevant to the understanding of the T2-low pathway.
The authors report a significant difference in BMI between T2-high and T2-low groups (30.2kg/m2 and 32.1kg/m2 respectively, P-value = <0.001). Whilst the...
We read with interest the recent paper from DJ Jackson et al, “Characterisation of patients with severe asthma in the UK Severe Asthma Registry in the biologic era” [1], and share their concerns regarding the risk of excessive corticosteroid exposure in T2-low individuals. We congratulate the authors for gathering such an extensive range of data in this large cohort of people with severe asthma, enabling meaningful comparisons, particularly between biologic and non-biologic populations. We echo the call for further work to identify and validate pragmatic T2-low endotype-specific biomarkers through clearer understanding of this inflammatory cascade. This cohort of patients continues to be under-served, made all the more evident by the paucity of novel therapies in this era of precision medicine.
We note the authors’ comments on T2-biomarker increase with corticosteroid dose reduction, and the presence of a historic T2-high profile in some individuals from the T2-low group. Whilst the postulated explanation reported by the authors, one of corticosteroid-induced T2-biomarker suppression, is undoubtedly a key factor (and indeed supported by the significant difference in corticosteroids between the groups), we would suggest another important factor that may be relevant to the understanding of the T2-low pathway.
The authors report a significant difference in BMI between T2-high and T2-low groups (30.2kg/m2 and 32.1kg/m2 respectively, P-value = <0.001). Whilst the absolute difference may seem trivial, raised BMI directly affects levels of commonly used T2-biomarkers [2,3,4]. With regards to fractional exhaled nitric oxide (FeNO), there is evidence for an adipose-mediated metabolic imbalance with nitric oxide synthase (NOS) uncoupling that affects NO bioavailability and leads to reduced FeNO [5]. In obesity-associated asthma, whilst total IgE and blood eosinophils correlate to T2-high profiles, this association is relatively weaker and total IgE levels are lower compared to healthy-BMI people with asthma. Furthermore, neither IgE, blood eosinophils nor FeNO predict sputum eosinophilia with current ranges, and evidence suggests lower thresholds are needed for T2-biomarkers in this cohort [4]. This has important implications for asthma phenotyping, monitoring of response to treatment and, as is a focus of the article, determining eligibility for advanced therapies. We would also suggest the difference in depression and anxiety seen within the two groups may be somewhat related to the BMI disparity.
Obesity-associated asthma remains incompletely understood, however it is becoming clearer that focus is needed on identifying biomarkers and specific treatments for severe T2 low asthma and this may be particularly relevant to the this population.
References
[1] – Jackson DJ, Busby J, Pfeffer PE, et al; UK Severe Asthma Registry. Characterisation of patients with severe asthma in the UK Severe Asthma Registry in the biologic era. Thorax. 2021Mar;76(3):220-227.
[2] – Komakula S, Khatri S, Mermis J, et al. Body mass index is associated with reduced exhaled nitric oxide and higher exhaled 8-isoprostanes in asthmatics. Respir Res. 2007Apr 16;8(1):32.
[3] – Renata Barros, André Moreira, João Fonseca, et al. Obesity and airway inflammation in asthma. Journal of Allergy and Clinical Immunology. 2006;117:6(1501-1502), ISSN 0091-6749,
[4] – Lugogo N, Green CL, Agada N, et al. Obesity's effect on asthma extends to diagnostic criteria. J Allergy Clin Immunol. 2018;141(3):1096-1104.
[5] – Holguin F, Grasemann H, Sharma S, et al. L-Citrulline increases nitric oxide and improves control in obese asthmatics. JCI Insight. 2019 Dec 19;4(24):e131733.
We thank the authors for their letter in response to our paper(1). We disagree however that the data among the tested subgroup are more informative than our other findings. This is because the small subgroup (1.1% of app users - 0.7% negative, 0.3% positive, 0.1% result unknown) who reported that they had undergone testing for COVID-19 at this relatively early stage in the pandemic (the month from 24th March 2020) were heavily selected. Testing policies focused on healthcare workers and others interacting with healthcare - in particular, patients tested who may have been attending healthcare settings for other, non-COVID-19 related, conditions. As numerous health conditions are smoking-related this would tend to increase the exposure of smokers without COVID-19 to testing. For these reasons, as discussed in the paper, the finding that smoking rates were lower in those testing positive is likely to be due to sampling bias. Rather than being “more relevant”, extrapolation from this subgroup to population risk is entirely inappropriate.
Show MoreThe letter does appear to misunderstand the groups presented – the “standard user” group were not asymptomatic during the study. Rather, as set out in the first paragraph of the results, they were individuals who at the point of registration with the Zoe COVID Symptom Study App did not think that they already had COVID-19. Among this group of “standard users” current smokers were more likely to report the onset of new symptoms suggesting...
The idea that smoking might have a protective effect against COVID-19 is an intriguing, man bites dog type of story, which gives it a certain attraction. Happily, it appears to be false and the assumption of harm has turned out to be correct[1-5].
Our data show clearly that in the 2.4 million Zoe COVID Symptom Study App users, people who smoked were at increased risk of symptomatic COVID-19[2] and were at risk of more severe disease, which is consistent with a systematic review of patients hospitalized with COVID-19[4]. Our findings are also consistent with The UCL COVID-19 Social Study3 which found increased risk of test confirmed COVID-19 (OR=2.14 (1.49–3.08)) and with the COVIDENCE study where smokers had an OR of1.42 (0.99-2.05) for test-confirmed COVID-19[1].
The OpenSafely dataset based on data from the primary care records of 17.3 million adults in the UK found that, adjusted for age and sex, also identifies smoking as a risk factor - current smoking was associated with a hazard ratio for COVID-19-related death of 1.14 (1.05–1.23)5. The apparently protective effect in the “fully adjusted” model is due to over-correction producing collider bias.
Since any protective effect of smoking in COVID-19 appears to be illusory, pursuing a mechanism for it is unlikely to be productive.
References
Show More1 Holt H, Talaei M, Greenig M, et al. Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK). medRxiv 2021:2021.2003...
There is no question that the harms of smoking hugely outweigh any potential health benefits. Many people, ourselves included, assumed at the beginning of the pandemic that greater susceptibility to COVID-19 would be another harm of tobacco smoking to be added to the long list. Surprisingly, most of the epidemiological data published over the last year do not support this claim. Indeed whereas ex-smokers are consistently found to be at increased risk of both SARS-CoV-2 infection and severe COVID-19, current smokers are consistently at lower risk than ex-smokers and in many studies they appear to be at a lower risk than never smokers. The lower infection rate in smokers compared to non-smokers and ex-smokers has been found across 62 studies (1, 2), including now a full cohort with a dose-response pattern (3).
The authors’ response does not counter the observation that among nearly 27,000 individuals who had a SARS-CoV-2 test in their study, smoking prevalence was lower in those who tested positive than in those who tested negative.
In the OpenSAFELY study (4) too, the direction of the association between smoking and death from COVID-19 depends critically on what adjustments are made. The primary analysis appears to be based on a fully adjusted Cox regression model in which the hazard ratio for current smokers relative to never smokers was 0.89 (95% CI 0.82-0.97). The value (1.14; 1.05-1.23) cited by Hopkinson and colleagues is after adjusting for age and sex...
Show MoreTo the editor,
We read the interesting report by Mason et al, “Respiratory exacerbations are associated with muscle loss in current and former smokers”.[1] In this study, the authors demonstrated that exacerbations are associated with accelerated loss of pectoralis muscles (PMs) in two large observational cohorts and quantified the impact of each annual exacerbation as the equivalent of 6 months of age-expected decline.
Show MoreSkeletal muscle loss is one of the major systemic manifestations associated with mortality in patients with COPD. Not only systemic muscle loss but also loss of specific muscle groups are associated with clinical outcomes such as exacerbations and mortality in patients with COPD.[2, 3] Moreover, muscle loss can occur heterogeneously.[4] This may be partially because each muscle group has its physiological function or biological characteristics such as muscle fiber composition. This supports that loss of specific muscle groups may have different implications in the clinical course of COPD.
We previously analyzed the cross-sectional area of erector spinae muscles (ESMCSA) and that of PMs (PMCSA) in male patients with COPD using chest CT.[3] ESMs are ones of antigravity muscles which are involved in maintaining an upright posture. PMs play an important role in the movement of upper limbs. Both muscles also act as accessory inspiratory muscles. ESMs are composed of 60% type 1 fibers and 40% of type 2 fibers and PMs are composed in the reverse...
With great interest we have read the study by Deshayes et al. The authors present two cases of silvernitrate (AgNO3) aspiration in laryngectomized patients.
Show More1) In both cases the applicator tip broke off.
2) The authors conclude that treatment should comprise oral antibiotics and one should refrain from bronchial washing with sodium chloride solution.
With this response we would like to reply to both points addressed above.
1.
Five years ago we were confronted with the aspiration of an AgNO3 applicator tip in a laryngectomized patient. After the incident we analyzed the case to prevent future AgNO3 applicator tip aspiration. The AgNO3 pencil, used in both our case and the cases in the current article, is specifically designed to treat dermal lesions like verruca which requires repeated use. The pencil therefore contains a relatively large volume of AgNO3. AgNO3 is a brittle substance. When the pencil is used with a little too much pressure there is risk for the tip to break, and when used in a tracheostomy, there is risk for aspiration.
Our case led us to immediately stop using the AgNO3 pencils for treatment of granulation tissue in a tracheostomy. We strongly recommend the use of disposable AgNO3 cutaneous sticks for the treatment of granulation tissue around a tracheostomy. The disposable sticks contains less volume of AgNO3. Moreover, the stick is easier to use in narrow spaces like a tracheostomy.
2.
Aspiration of AgNO3 i...
We thank Tanimura and colleagues for their thoughtful commentary on our recent manuscript, “Respiratory exacerbations are associated with muscle loss in current and former smokers” and read their analysis of erector spinae muscle area (ESMA) with interest (1). In their commentary, they note that muscle loss can occur heterogeneously, with the greatest expected impact on the muscles of ambulation. They suggest that erector spinae muscles, due to their fiber composition and anti-gravity role, are a better reflection of inactivity-related muscle loss and posit that changes in pectoralis muscle area (PMA) may only reflect changes in nutrition (as measured by body mass index, BMI).
We agree that muscle loss is unlikely to be uniform; however, a disconnect has been reported between the postural muscles of the trunk and ambulatory muscle (e.g. quadriceps) weakness, despite similar fiber types (2). Few studies measure both groups of muscles simultaneously, but there is evidence that inspiratory force is more affected than peripheral muscle force in patients with COPD; implying that deconditioning is not the sole driver of muscle dysfunction (3). While the pectoralis muscle potentially underestimates inactivity-related atrophy, these studies suggest its role as an accessory muscle of inspiration makes it a reasonable target for capturing any underlying systemic process.
In contrast to Tanimura et al’s findings, in the COPDGene participants (n=8,603) BMI was more stro...
Show MoreWe thank Brennan et al, for sharing their experiences. In contrast to our observed reduction of more than 50% in AECOPD hospital admissions over a 6-month period, Brennan and colleagues observed a reduction of only 18% over a 4-month period. In addition, while we saw a significant and sustained decrease, Brennan et al. observed a decrease only in the first month following lockdown. At the fundamental level, respiratory viruses can spread either via contact, droplet or aerosols[1] and thus in theory mask wearing, social distancing and increased personal respiratory etiquette and community hygiene would reduce transmission and contribute to reduced incidence of AECOPD. The use of masks has been shown to reduce exposure to acute respiratory viruses by 46%[2].
We hypothesise that these differences could potentially be due to variations in the degree of adherence to mask wearing/social distancing, as well as nuances in public health measures introduced in various countries during the COVID-19 pandemic.
For instance, Singapore had mandated face-mask wearing in April 2020. The observations reported by Brennan et al terminated in June 2020 while Ireland only mandated face-mask wearing in August 2020. and hence may not have captured the impact of compulsory mask wearing. The difference in timing of implementation and enforcement of government policies during the COVID-19 pandemic possibly contributed to a different experience in Ireland.
Aside from early impleme...
Show MoreWe read with interest the recent study by our colleagues Tan et al (1) which reported the introduction of public health measures during the pandemic, such as social distancing and universal mask wearing, were observed to coincide with a marked reduction in transmission of other circulating respiratory viral infections. They reported a reduction in hospital admissions with acute exacerbation of COPD (AECOPD) by over 50% during the six month period of the pandemic from February to June 2020. They supported this observation with microbiological data showing a significant reduction in PCR-positive respiratory viral infections compared to the pre-pandemic era.
Ireland has the highest rate of hospitalisations for AECOPD in all OECD Countries (2). The first case of COVID-19 in the Republic of Ireland was reported on 29/02/2020 and stringent public health measures were introduced in mid-March to combat the spread (3).
We wish to describe our experiences of hospital admission with AECOPD during the first wave of the pandemic in a tertiary referral hospital in the West of Ireland. In our clinical practice, we noticed a reduction in patients admitted with COPD exacerbations at the beginning of the pandemic. We aimed to evaluate the impact of these infection control measures on our COPD population.
We conducted a retrospective cohort study of electronic health care records of patients who were hospitalised with a primary diagnosis of AECOPD over the four-month per...
Show MoreThe influence of obesity on both asthma and T2 biomarkers remains poorly understood and we fully agree this requires further investigation, as does the relationship between obesity, depression and persistent symptoms of breathlessness. However, the data correlating obesity and FeNO is conflicting and the reported weak positive associations have often not been adjusted for corticosteroid dose and may simply reflect higher doses of corticosteroid therapy in more breathless obese patients than by those of normal weight, rather than a specific mechanistic relationship.
Show MoreMoreover, the UKSAR population appears very different from the cohorts described in some of these reports. For example, the average FeNO was only 25ppb in the Komakula study, whilst in the study by Lugogo subjects were predominantly T2-low across all BMI categories: the upper quartile value of blood eosinophils in both lean and obese groups was <300 cells/µL, whilst the upper quartile of FeNO in both lean and obese groups was <30ppb. In contrast, even in the UKSAR T2 high cohort, the mean BMI was in the obese range.
The nature and veracity of the ‘T2-low’ phenotype remains unclear, particularly in severe asthma. What is increasingly apparent is that patients are frequently prescribed high dose inhaled and systemic corticosteroids for respiratory symptoms, which suppresses T2 inflammation in the process. In the context of obesity and other co-morbidities known to be associated with increased re...
We read with interest the recent paper from DJ Jackson et al, “Characterisation of patients with severe asthma in the UK Severe Asthma Registry in the biologic era” [1], and share their concerns regarding the risk of excessive corticosteroid exposure in T2-low individuals. We congratulate the authors for gathering such an extensive range of data in this large cohort of people with severe asthma, enabling meaningful comparisons, particularly between biologic and non-biologic populations. We echo the call for further work to identify and validate pragmatic T2-low endotype-specific biomarkers through clearer understanding of this inflammatory cascade. This cohort of patients continues to be under-served, made all the more evident by the paucity of novel therapies in this era of precision medicine.
Show MoreWe note the authors’ comments on T2-biomarker increase with corticosteroid dose reduction, and the presence of a historic T2-high profile in some individuals from the T2-low group. Whilst the postulated explanation reported by the authors, one of corticosteroid-induced T2-biomarker suppression, is undoubtedly a key factor (and indeed supported by the significant difference in corticosteroids between the groups), we would suggest another important factor that may be relevant to the understanding of the T2-low pathway.
The authors report a significant difference in BMI between T2-high and T2-low groups (30.2kg/m2 and 32.1kg/m2 respectively, P-value = <0.001). Whilst the...
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