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4th August 2022
The lung cancer risk of both current light and former heavy smokers for whom screening with a computer tomography (CT) scan is not currently recommended appears to be 10-folder higher than those who have never smoked according to the findings of a study by US researchers.
The World Health Organization estimates that across the globe in 2020, there were 2.21 million cases of lung cancer and which led to 1.8 million deaths. Thus attempts to screen for the early signs of lung cancer might potentially reduce the number of lung cancer deaths. The development of low-dose helical computed tomography (CT) scanning has shown that low-dose CT enables the detection of many lung cancer tumours at an early stage. In fact, a recent trial concluded that among high-risk individuals who underwent CT screening, lung-cancer mortality was significantly lower compared to those who did not undergo screening. Consequently, screening recommendations with low-dose computed tomography (LDCT) have been produced and suggest annual screening for adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years. Screening, however, is not advocated for former smokers with a 20 pack-year or greater smoking history who quit 15 or more years ago (former heavy smokers) or for current smokers with a smoking history of 20 pack-years or less (current non-heavy smokers). The reasons for excluding these two groups is not clear but presumably is related to an expected lower lung cancer risk. But how valid is this recommended exclusion? This was the basis for the present study in which researchers sought to examine the level of cancer risk among these two groups for whom screening is not recommended.
The researchers used patient data from the Cardiovascular Health Study which enrolled nearly 6,000 community-dwelling older adults (65 years and older) although their analysis was restricted to individuals who were free of cancer at enrolment and for whom pack-year smoking history and smoking cessation data were available. The main outcome of interest was incident lung cancer during follow-up.
Lung cancer risk over time
A total of 4279 participants with a mean age of 72.8 years (57.3% female) were included and followed for a median of 13.3 years. There were 861 current non-heavy smokers and 615 former heavy smokers and 1,973 never smokers who were used as the reference point.
During follow-up, lung cancer occurred in 0.5% of never smokers, 5% of current non-heavy smokers and 5% of former heavy smokers.
The age-adjusted hazard ratio (HR) for incident lung cancer for current non-heavy smokers was 10.06 (95% CI 3.41 – 29.70) and 10.22 (95% CI 4.86 – 21.50) for former heavy smokers, i.e., the two groups for whom screening is not recommended. The mortality risk for current, non-heavy smokers was 53% higher (HR = 1.53, 95% CI 1.22 – 1.92) and 18% higher (HR = 1.18, 95% CI 1.05 – 1.32) for former heavy smokers.
The authors concluded that there appears to be a very high lung cancer risk among those who are excluded from the recommendations for CT screening and called for future studies to examine whether annual screening could reduce lung cancer mortality in these populations.
Faselis C et al. Assessment of Lung Cancer Risk Among Smokers for Whom Annual Screening Is Not Recommended JAMA Oncol 2022
1st October 2021
Throughout the COVID-19 pandemic, several important risk factors for more severe disease have become apparent including male sex, increased age and cardiovascular co-morbidities. Whether or not being a current smoker affects COVID-19 outcomes is less clear with some analyses indicating a higher risk of worse outcomes, whereas others reviews suggest a reduced risk . One solution to untangling this ambiguity is the adoption of a dual analytical approach to data analysis, for example, by comparing the results from observational studies with those from a Mendelian randomisation study. In a Mendelian randomisation (MR) study, the underlying assumption is that a genetic variant influences only the variable of interest and since genetic variants are randomly allocated at birth, MR is less susceptible to confounding which is a problem in observational studies. Genetic analysis can be used to identify variants associated with particular traits. For example, tobacco and alcohol use are known to have heritable behaviours and in one study, researchers identified 566 genetic variants in 406 loci associated with multiple stages of tobacco use, e.g., initiation, cessation, and heaviness (i.e., the number of cigarettes smoked per day).
Using a dual analytical approach to examine the relationship between smoking and outcomes in COVID-19, a team from the Nuffield Department of Primary Care Health Sciences, University of Oxford, UK, turned to data held within the UK Biobank. The researchers separately explored the relationship between smoking and COVID-19 using findings from both observational studies and Mendelian randomisation. For the MR study, researchers used established genetic proxies for smoking initiation and smoking heaviness (i.e., the number of cigarettes smoked per day). The results were analysed using multivariate logistic regression analysis adjusted for several variables including age, sex, ethnicity and several co-morbidities.
For the observational analysis, there were 421,469 individuals with a median age of 68.6 years (55.1% female). Current smokers were found to have an overall higher risk of hospitalisation (adjusted odds ratio, aOR = 1.80, 95% CI 1.26 – 2.29) and mortality (aOR = 4.89, 95% CI 3.41 – 7.0).
Similarly, in the Mendelian randomisation analysis, genetic propensity to initiate smoking was associated with a higher risk of hospitalisation (aOR = 1.60, 95% CI 1.13 – 2.27), but not mortality (aOR = 1.35, 95% CI 0.82 – 2.22, p = 0.23). For genetically predicted higher number of cigarettes smoked per day, the risk of hospitalisation was also higher (aOR = 5.08, 95% CI 2.04 – 12.66) and risk of death (aOR = 10.02, 95% CI 2.53 – 39.72).
Discussing their findings, the authors said that this was the first study to adopt a dual analytical approach to assess the relationship between smoking and outcomes from COVID-19. They concluded that the congruence between the two methods indicated that a lifelong predisposition to smoking and smoking heaviness supported a causal effect (found in the observational analysis) of smoking on COVID-19 severity.
Clift AK et al. Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort. Thorax 2021
18th January 2021
Since March 2020, members of the public in the UK have been able to download and register with a smartphone app as part of an ongoing COVID-19 symptom study. Individuals who register with the app self-report demographic and healthcare data (i.e., co-morbidities) as well as any COVID-19-related symptoms. The app has been downloaded by over 2.4 million people and for the present analysis, researchers collected COVID-19-related symptom data between March and April 2020. Participants were asked to report if they felt ‘physically normal’ and those answering no were invited by the app to record the presence of 14 symptoms that have been associated with COVID-19. The main outcome for the study was the development of ‘classic’ symptoms of COVID-19, i.e., fever, new persistent cough and breathlessness and the association of this symptom triad with current smoking status. They also explored the relationship between smoking, a positive PCR test result and hospital attendance.
During the study period there were 2,401,982 registered users (63.3% female) with a mean age of 43.6 years, of whom, 834,437 (35%) reported not feeling ‘physically normal’. The team then classified all registered participants into one of four groups; those who had tested positive for COVID-19, (SC2P); those who tested negative (SC2N); those self-reporting COVID-19 symptoms and who thus believed they had the virus (SC2S); the remainder who made up a group termed ‘standard users’. Among standard users, current smokers were more likely to report developing the COVID-19 triad symptoms, suggesting a diagnosis of infection (odds ratio, OR = 1.14, 95% CI 1.10 – 1.18, p < 0.001) and to report a higher symptom burden (defined as reporting > 5 of the 14 symptoms), than non-smokers. Smoking was also associated with a higher symptom burden in both the SC2S and SC2P groups. Interestingly, while smoking rates were lower among those testing positive for the virus, i.e., in the SC2P group (thus suggesting a protective effect against the virus from smoking), their reported symptom burden was higher than non-smokers. Furthermore, smokers in the SCP2 group had a higher risk of attending hospital due to COVID-19 compared to non-smokers (OR = 2.11 95% CI 1.41 – 3.11, p < 0.001) and this association remained even after adjusting for co-morbidities (OR = 1.87).
Summarising these findings, the authors made the following observations. Smoking was associated with an increased risk of developing self-reported COVID-19 symptoms and a greater symptom burden. A higher symptom burden was also evident among smokers testing positive for the virus and that these individuals were more likely to visit hospital because of their symptoms.
The authors concluded by calling for smoking cessation to be a part of public health campaigns during the current pandemic.
Hopkinson NS et al. Current smoking and COVID-19 risk: results form a population symptom app in over 2.4 million people. Thorax 2021