Ben-Gurion University of the Negev Researchers (BGU) have found for the first time that cigarette smoke toxicity impacts the protective biofilm in the lungs, particularly concerning when paired with COVID-19 respiratory issues.
Though many health factors are known about smoking, little is known about the overall toxicity potential of its ingredients. Researchers developed a new smoke testing system called a bacterial panel with genetically modified bioluminescent bacteria to measure both filtered and unfiltered cigarette smoke’s complex molecular mixture.
According to the new study published in the journal Talanta, the researchers found that cigarette smoke affects communication between bacteria, which can affect microorganisms in the body and cause a negative effect on the formation of biofilm, which protects lung bacterial colonies.
The study examined 12 distinct types of commercial cigarettes of varying prices bought at local Israeli stores, revealing that filters helped somewhat in lowering toxicity.
“The experiment proved that the filter is a crucial element in reducing the harm of smoking so therefore, new filters need to be developed to reduce toxicity,” explains Prof. Robert Marks, head of the BGU Avram and Stella Goldstein-Goren Department of Biotechnology Engineering.
Prof. Robert Marks is a leading expert in the study of genetically engineered bacteria. His work focuses on finding the specific mechanisms of toxins in a variety of materials and their impact on the environment.
Tobacco companies, research organizations, and academics can use the bacterial panel and its accompanying system to cost-effectively monitor the overall toxicity of various commercial cigarettes and test their filter effectiveness.
“The recently developed smoke testing system, based on our bacterial panel, is a new system for researchers that need to analyze toxicity of smoke at a reasonable cost,” says Prof. Marks.
The association between smoking and COVID-19 has generated a lot of interest in the research community. Smoking is an established risk factor for respiratory infections [1]. Therefore, it was not surprising that reports suggested a higher risk for severe COVID-19 among hospitalized smokers [2,3,4].
However, these studies failed to notice the relatively low prevalence of smoking among hospitalized patients compared to population smoking rates [5, 6]. This was first noticed in Chinese case series, but similar findings have been observed in other countries, while it has also been reported that smoking may be associated with lower susceptibility for SARS-CoV-2 infection [7,8,9,10].
The possibility that smokers may be less likely to develop severe COVID-19 that would require hospitalization is an important factor in determining the overall smoking-related risk. A higher risk for adverse outcome among hospitalized smokers is not applicable to all smokers if they are indeed less likely than non-smokers to be hospitalized for COVID-19.
In March, we hypothesized for the first time that nicotine may be protective against COVID-19 due to its anti-inflammatory properties and to a potential direct interaction between SARS-CoV-2 and nicotinic acetylcholine receptors [11, 12]. The cholinergic anti-inflammatory pathway represents a reflex mechanism that modulates the immune response and protects from hyper-inflammation, a hall mark of severe COVID-19 [13, 14].
Therefore, if the virus interacts with the cholinergic system, dysregulation of the cholinergic anti-inflammatory pathway could result in an uncontrolled immune response. This hypothesis is not contradictory to reports of a higher risk for adverse outcome in hospitalized smokers with COVID-19.
Smokers experience abrupt cessation of nicotine intake once hospitalized (unless nicotine replacement therapies are administered), resulting in the rapid elimination of plasma nicotine levels and deprivation of any hypothetical beneficial effects.
Recently, Karanasos et al. [15] published a systematic review and meta-analysis of 22 studies, examining the impact of smoking on disease severity and mortality of hospitalized patients with COVID-19 infection. They also performed a meta-regression analysis and stratified studies according to the prevalence of diabetes among patients (< 15% and ≥ 15%).
They reported that smoking was associated with higher odds of disease severity in studies with low prevalence of diabetes. However, the authors did not examine the smoking prevalence among hospitalized COVID-19 patients relative to the population smoking rates.
Additionally, we noticed minor errors in the data presented (mentioned below) which were addressed in the present analysis. Finally, the authors used a fixed-effects method for the meta-analysis. This is rather odd and probably inappropriate, especially when it comes to the justification they proposed. The authors stated that they used fixed effects due to non-significant heterogeneity (I2 < 50%).
This particular approach is questionable since the I2 purpose is to quantify the degree of heterogeneity and not to test its significance. Indeed, the respective statistical test based on the Cochran’s chi-square yielded a significant p value of 0.02. (Note that this test has low power and thus a significant result is even more important).
That is, if the authors were to choose based on purely statistical arguments, they should have chosen the random-effects model. Nevertheless, the choice of fixed vs. random effects has been a matter of debate in the literature and the prevailing approach is that the model choice for meta-analysis should be based on the sampling frame and not on the results of a statistical test such as the test for heterogeneity in effect sizes [16, 17].
The studies analyzed included patients from different hospitals, geographical locations and countries, and age and comorbidities. Additionally, even the definition of smoking was not universal in all studies, with some reporting current and former smoking while others reporting “smoking history” or “smoking” [13].
Taking into account that the primary goal of such an analysis is to generalize the results, one would argue that the random-effects model should have been the method of choice in the first place, irrespective of the identified heterogeneity [16]. For these reasons, the random-effects model is considered more appropriate and is advocated by most experts [16, 18, 19].
We also need to emphasize that in case of zero heterogeneity the estimates of both models coincide. Moreover, the choice of the fixed-effects model comes to a direct disagreement with the subsequent use of a random-effects meta-regression performed by the authors [15]. Meta-regression is used to explore the sources of heterogeneity [20]; thus, its use contradicts the initial argument for choosing the fixed-effects model.
Considering the above, and to address potential errors in the original study, we re-analyzed the data and expanded the analysis by: (1) calculating the prevalence odds ratio (POR) [21] of smoking among hospitalized COVID-19 patients relative to population smoking rates and (2) examining the association between smoking and COVID-19 severity and mortality, as well as the association between smoking and severity with studies stratified according to diabetes prevalence (< 15% and ≥ 15%).
reference link: https://harmreductionjournal.biomedcentral.com/articles/10.1186/s12954-020-00437-5
More information: Tim Axelrod et al, Cigarette smoke toxicity modes of action estimated by a bioluminescent bioreporter bacterial panel, Talanta (2021). DOI: 10.1016/j.talanta.2020.122076
