Content uploaded by Alberto Nájera López
Author content
All content in this area was uploaded by Alberto Nájera López on May 08, 2020
Content may be subject to copyright.
Abstract: SARS-CoV-2 is a new coronavirus that has caused a worldwide pandemic. It produces
severe acute respiratory disease (COVID-19), which is fatal in many cases, characterised by
cytokine release syndrome (CRS). According to the World Health Organization, those who smoke
are likely to be more vulnerable to infection. Here, in order to clarify the epidemiologic relationship
between smoking and COVID-19, we present a systematic literature review until 28 April 2020 and
a meta-analysis. It includes 18 recent COVID-19 clinical and epidemiological studies based on
smoking patient status from 720 initial studies in China, USA, and Italy. The percentage of
hospitalised current smokers was 7.7% (95%CI: 6.9-8.4) in China, 2.3% (95%CI: 1.7-2.9) in the USA
and 7.6% (95%CI: 4.2-11.0) in Italy. These percentages were compared to the smoking prevalence of
each country and statistically significant differences were found in them all (p <0.0001). By means
of the meta-analysis, we offer epidemiological evidence showing that smokers were statistically
less likely to be hospitalised (OR=0.18, 95%CI: 0.14-0.23, p<0.01). CRS and exacerbated
inflammatory response are associated with aggravation of hospitalise patients. In this scenario, we
hypothesise that nicotine, not smoking, could ameliorate the cytokine storm and severe related
inflammatory response through the cholinergic-mediated anti-inflammatory pathway.
Keywords: COVID-19; SARS-CoV-2; current smoker; smoking; smoker; hospitalized; nicotine;
cytokine storm.
1. Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the new coronavirus that first
broke out in Wuhan (Hubei Province, China) in December 2019, has quickly spread and become a
global pandemic [1,2]. SARS-CoV-2 is the third coronavirus outbreak of this century, following
severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome
coronavirus (MERS-CoV) [3]. Coronavirus disease 2019 (COVID-19) causes clinical manifestations
that range from mild respiratory symptoms to severe pneumonia, can be fatal in many cases, and is
aggravated by cytokine release syndrome (CRS) or cytokine storm [4].
It has been well established that smokers are at a significantly high risk of chronic respiratory
disease and acute respiratory infections, and current smokers are at more risk of developing
Lydia.Jimenez@uclm.es;
Tel: (+34) 926295300
(J.D.N. and L.J.D.).
*
Correspondence:
Alberto.Najera@uclm.es;
Tel: (+34) 967599325
(A.N.),
Juan.Navarro@uclm.es,
#
Contributed equally.
Lydia.Jimenez@uclm.es, Juan.Navarro@uclm.es
5
University of Castilla-La Mancha, Centre for Biomedical Research, School of Medicine. Ciudad Real, Spain;
4
Gerencia
de Emergencias Sanitarias, Salud de Castilla y Leon, Spain;
3
Gerencia de Atención Primaria, Salud de Castilla y Leon, Avila, Spain;
Real, Spain;
2
Hospital General La Mancha Centro, Servicio de Salud de Castilla-La Mancha. Alcazar de San Juan, Ciudad
Jesus.Gonzalez@uclm.es, Alberto.Najera@uclm.es
1
University of Castilla-La Mancha, Centre for Biomedical Research, School of Medicine, Albacete, Spain;
Lydia Jimenez-Diaz
5,#,*, Juan D.
Navarro-Lopez
5,#,*
and
Alberto Najera
1,#,*
Jesus Gonzalez-Rubio
1,#,
Carmen Navarro-Lopez
2, Elena Lopez-Najera
3, Ana Lopez-Najera
4,
Systematic
Review
and a
Meta-Analysis
What is
Happening
with
Smokers
and COVID-19? A
Review
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
© 2020 by the author(s). Distributed under a Creative Commons CC BY license.
2 of 12
influenza than non-smokers [5]. Smoking is also closely associated with MERS-CoV [6], but there is
no clear evidence for this association with SARS-CoV-2 [7].
In today’s pandemic caused by coronavirus 2019 (COVID-19), some clinical characteristics have
been described, but not without controversy about the effects of tobacco [8–12]. All this suggests that
a smoking habit background is a poor prognosis factor in infected patients [13], or smokers could be
more prone to contagion [13–15]. As evidence is lacking, the effect that tobacco has on contagions,
number of hospital admissions and the seriousness of smoking patients is unclear [15].
It is worth remembering that smoking kills around eight million people worldwide every year
[16], irrespectively of any interaction with COVID-19, which is why smoking cessation is an urgent
priority. Nonetheless, clinical data published until the time of the COVID-10 outbreak in China, as
well as the first date made public in the USA [17,18] and Italy [19], indicate that the number of
smokers hospitalised for COVID-19 was perceptibly lower than expected if we bear in mind the
prevalence of smoking in these countries, and even despite the possible biases in reports [17,20,21].
In China, the mean proportion of smokers is 26.1%. Among males, 54.0% are current smokers,
and only 2.6% among women [22]. In the USA, the proportion of smokers is 15.6% in males and
12.0% in females, with a combined proportion of 13.7% [23]. the proportion of smokers in Italy is
19%, with 23.3% in males and 15.0% in females [24]. So, a similar or higher percentage of current
smokers hospitalised with SARS-CoV-2 is expected to appear, with males predominating.
As this virus has only recently appeared, very few studies have evaluated possible risk factors,
including the effect of tobacco. Given the existing gaps in evidence, we carried out a systematic
review and a meta-analysis of studies about COVID-19, which includes information about the
smoking habit (current smokers) of patients hospitalised in China, USA, and Italy to evaluate the
relation between smoking and COVID-19.
2. Methods
2.1. Literature search strategy
The systematic review was carried out according to the Referred Reporting Items for Systematic
Review and Meta-Analysis (PRISMA) and MOOSE guidelines [25,26]. A flow chart is provided in
Figure 1. A systematic searched was made of the ISI Web of Science
(http://www.webofknowledge.com) for the relevant works published until 28 April, 2020.
The following search terms were used: [‘COVID 19’ OR ‘NCOV 19’ OR ‘sars cov-2’ OR ‘sars cov
2’ OR 'novel coronavirus'] AND [‘smoking’ OR ‘tobacco’ OR ‘smoker*’ OR 'risk factor' OR 'clinical
features' OR 'clinical characteristics'].
2.2. Inclusion and exclusion criteria
In a first phase, any duplicated works and those not written in English were excluded. Then the
studies that did not provide clinical characteristics were removed, or those describing diagnosis
techniques, therapies, modelling, strategic response, imaging, genetics, biology, transmission
mechanisms, healthcare workers protection, surveillance, scenarios, animal, genomics, those about
asymptomatic patients, skin lesions and lesions specific of other organs, data on children or breastfed
infants, among others. In the next phase, the works that provided no details about smokers were
removed, especially those with no data on “current smokers”. Finally, certain types of articles were
excluded from the meta-analysis, e.g. comments, letters, editorial, viewpoint, correspondence, etc.,
which included no detailed data about smoking patients. However, they were considered to perform
the qualitative analysis along with three systematic reviews and meta-analyses.
2.3. Data Extraction
Records were checked for duplicates using Zotero 5.0.85 (http://www.zotero.org). Two
independent reviewers (AN and JGR) screened the literature search and assessed each study to be
included by reading titles, abstracts and full texts. Any disagreement was solved in conference with
the support of a third author (JN). Relevant data were acquired from each eligible study by means of a
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
3 of 12
structured extraction sheet, which was prepared and approved by all the reviewers’ by reaching a
consensus after screening the eligible studies.
Figure 1. Flow chart showing how studies were identified and selected.
2.4. Statistical analysis
Data analyses were performed using the meta packages in R (Software R-3.6.3). A random-effects
meta-analysis was used to calculate the pooled estimated prevalence with 95% confidence intervals
(95%CI). A Chi-square test or Fisher's exact test was carried out to compare the differences between the
observed and expected current smokers for all the studies individually and by combining all the data.
Heterogeneity between studies was assessed by the Cochran Chi-square test and I2. Depending
on the I2 value, a fixed-effects (less than 50%) or a random-effects (more than 50%) model was used.
3. Results
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
4 of 12
3.1. Literature retrieval
The literature search gave 720 articles. Removing duplicate documents (n=14) and those not
written in English (n=34) left 672 items. Then selection was performed by reading titles and abstracts
(469 were excluded). Finally, publications were selected by applying the final selection criteria
(detailed current smoker data and hospitalised patients). Of the remaining 203 works, 41 included
data about smoking habit or a smoking background with the last inclusion criterion: the work
should provide details of the proportion of smokers by specifying current smokers and hospitalised
patients.
This procedure gave 18 experimental documents: 15 papers with data on the China outbreak
[27–41], one official report with preliminary data on the USA outbreak [17], in New York city [18]
and the Italian outbreak [19]. We provide more details in Tables 1 and 2 and in the flow chart (Fig. 1)
to make this search repeatable in the future.
Table 1. Comparison of the hospitalised current smokers in the Chinese COVID-19 outbreak. The
combined analysis is the result of adding all the individual studies. The expected current smokers
were estimated using 54% and 2.6% for males and females, respectively [22].
N
(male/female)
current
smokers
95%CI
expected current
smokers
(male/female)
Sig.
Chen et al., 2020
274 (171, 103)
12 (4·4%)
[2·0-6·8]
95· 0 (92·3, 2·7)
p<0·0001
Guan et al., 2020
1085 (631, 454)
137 (12·6%)
[10·7-14·6]
352·5 (340·7, 11·8)
p<0·0001
Han et al. 2020
17 (6, 11)
3 (17·6%)
[-0·5-35.8]
3·5 (3·2, 0·3)
p=0·9999
Huang et al., 2020
41 (30, 11)
3 (7·3%)
[-0·7-15·3]
16·5 (16·2, 0·3)
p=0·0006
Jin et al., 2020
651 (320, 331)
41 (6·3%)
[4·4-8·2]
181·4 (172·8, 8·6)
p<0·0001
Li et al., 2020
548 (279, 269)
41 (7·5%)
[5·3-9·7]
157·7 (150·7, 7·0)
p<0·0001
Lian et al., 2020
788 (407, 381)
54 (6·9%)
[5·1-8·6]
229·7 (219·8, 9·9)
p<0·0001
Mo et al., 2020
155 (86, 69)
6 (3·9%)
[0·8-6·9]
48·2 (46·4, 1·8)
P<0·0001
Wan et al., 2020
135 (72, 63)
9 (6·7%)
[2·5-10·9]
40·5 (38·9, 1·6)
p<0·0001
Wang et al. 2020
125 (71, 54)
16 (12·8%)
[6·9-18·7]
39·7 (38·3, 1·4)
p=0·0003
Yao et al., 2020
108 (43, 65)
4 (3·8%)
[1·0-7·3]
24·9 (23·2, 1·7)
p<0·0001
Zhang, Dong et
al., 2020
140 (69, 71)
2 (1·4%)
[-0·5-3·4]
39·1 (37·3 ,1·9)
p<0·0001
Zhang, Cai et al.,
2020
645 (328, 317)
41 (6·4%)
[4·5-8·2]
185·4 (177·2, 8·2)
p<0·0001
Zhang, Ouyang et
al., 2020
120 (43, 77)
6 (5·0%)
[1·1-8·9]
25·2 (23·2, 2·0)
p=0·0004
Zhou et al., 2020
191 (119, 72)
11 (5·8%)
[2·5-9·1]
66·2 (64·3, 1·9)
p<0·0001
Combined
5,023 (2675,
2348)
386 (7·7%)
[6·9-8·4]
1,505·6 (1444·5, 61·0)
p<0·0001
3.2. China
As previously mentioned, all the studies included in the analysis contained detailed data about
hospitalised current smokers. All the patients had been diagnosed with COVID-19 by PCR tests.
Most studies were conducted in the Hubei province [27,29,31,33,36,39–42], three in the Zhejiang
province [30,32,38], one in the Anhui province [35] and another in the Chongqing province [34]. One
study had collected data from 30 provinces [28] and from 522 hospitals. In general, most of the
studies collected data from patients in only one hospital. Almost all the works included in the
meta-analysis were retrospective, one was prospective [40] and one was ambispective [31]. Their
collected data were taken between 11 December, 2019 and 12 February, 2020. Data were generally
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
5 of 12
taken from electronic medical records, except one work, which collected them directly by personally
communicating with patients [35]. The studies homogeneously reported clinical and
epidemiological data, and included patients, for example, in the order in which they arrived at
hospital. However, one of the studies included 17 patients who had been discharged from hospital
[29] and included the highest percentage of current smokers (12.6%). Three other studies recruited
patients according to some selection criterion, or because they presented abnormal imaging findings
[38], had previously visited the Huanan seafood market [40] or were older patients [43].
Table 1 presents the data that correspond to the 15 included studies. They all provide details of
the total proportion of males and females, and the number of current smokers. The expected
smokers values were calculated with these details, the proportion of males and females in each
study and the smoking prevalence in China [22]. The 95% confidence interval (95%CI) of the
percentage of smokers estimated with the observed values was also included. In all cases,
statistically significant differences (p<0.001) appeared between the observed and expected values,
except for the study by Han et al. 2020, whose sample included only 17 patients (p=0.9999). The
combined values were obtained by adding all the patients in each study to consider a total sample of
4,795 patients, of whom 376 were current smokers. The prevalence percentage of current smokers
was 7.7% (95%CI: 6.9-8.4%). Once again, the observed difference was very significant (p<0.0001)
compared to the expected values. This value was much lower than the expected one when
considering the prevalence in China (54% in males, 2.6% in females, and a combined 26.1%).
3.2.1. Meta-analysis in China
Figure 2 offers the meta-analysis results. The obtained heterogeneity (I2) was 64%, so the
selected model was the random model (p<0.01), which gave an odds ratio value of 0.17 and a 95%CI
of 0.13-0.22.
Figure 2. Meta-analysis of the Chinese studies.
3.3. USA and Italy
Only three studies not conducted in China were included: two from the USA with official data
from Centers for Disease Control and Prevention (CDC) and New York city [17,18]; one from Italy
[19]. As numbers are small, they are all presented in this section (Table 2). In all, the two US studies
included 2,412 hospitalised patients, of whom 55 were current smokers (1.7% and 5.1%, respectively),
although no gender proportions were provided in the CDC study. The Italian study recruited 236
patients, of whom 18 were current smokers (7.6%). All the patients’ COVID-19 diagnosis had been
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
6 of 12
confirmed by laboratory tests, in which case the US studies employed an official report [17] and a
comment to the Editor [18], but provided detailed information about current smokers.
Table 2. Comparison of the hospitalised current smokers in the COVID-19 outbreaks in the USA and
Italy. To calculate the expected current smokers values in the USA, 15·6% in males and 12·0% in
females were taken, which gave a combined 13·7% [23]. In Italy, 23.3% in males and 15.0% in females
were taken [24].
N (male/female)
current
smokers
95%CI
expected current
smokers
(male/female)
Sig.
CDC, 2020
20191
35 (1·7%)
[1·2-2·3]
278·6
p<0·0001
Goyal et al., 2020
393 (238, 155)
20 (5·1%)
[2·9-7·3]
55·7 (37·1, 18·6)
p<0·0001
USA, combined
2,412
55 (2·3%)
[1·7, 2·9]
334·3
p<0·0001
Colombi et al., 2020
236 (177, 59)
18 (7·6%)
[4·2-11·0]
50·1 (41·2, 8·9)
p<0·0001
1 Gender proportions not specified.
When comparing the observed and expected values according to smoking prevalence in each
country, the differences were very statistically significant in all cases (p<0.0001). This result was also
obtained when the expected proportion was analysed by considering the combination of the two US
studies.
3.4. Global meta-analysis
Figure 3 provides the meta-analysis results of the 18 studies included in the systematic review.
The resulting heterogeneity was I2=69% (p<0.01), so the random model that provided an odds ratio
of 0.18 and a 95%CI of 0.14-0.22 was selected.
The meta-analysis results (OR) revealed statistically significant differences in 17 of the 18
studies and in the combined total (p<0.01). Only one study did not show these differences, that by
Han et al. (2020).
Figure 3. Global Meta-analysis.
4. Discussion
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
7 of 12
This work takes data from 18 studies conducted in different parts of the world, but mainly
China. They describe the number of current smokers hospitalised and with a confirmed COVID-19
diagnosis. All the studies included in the meta-analysis provide details of patients’ smoking
background, which allowed the number of current smokers. This is very important because the other
studies excluded from the analysis, despite having recruited lots of patients, did not provide
information about smoking background [20].
In each case, these data were compared to the prevalence of smokers in each country by
considering the proportion of males and females whenever possible. In every case except one, which
had the fewest patients, very statistically significant differences were observed (p<0.001) and would
indicate that something is happening with COVID-19 incidence in smokers.
Both the systematic review and the presented meta-analyses have some limitations. The
heterogeneity in the meta-analysis was determined as I2=64% in the Chinese studies and as I2=69%
when summing the US and Italian works. The effect of some studies on heterogeneity was explored.
Heterogeneity considerably lowered when the work by Guan et al. (2020) was eliminated (I2=36%
for the set of Chinese works and I2=56% in the global meta-analysis). This analysis is not provided in
the results.
It was not possible to perform a detailed study using the age groups of current smokers,
although all patients were adults. As smoking habit prevalence changes with age, mean values were
used. With males, this value could vary with age from 41.5% (males aged 70 years) and 60.3% (males
between 40-49 years old) in China [21]. Conversely, these values for females were much lower, and
varied between 1.2% (aged 18-29 years) and 5.8% (older than 70). The number of males and females
was similar in practically all the studies. Generally speaking, more male patients were included in all
the studies, they smoked more heavily and were at higher risk of suffering the disease [44]. In
females, if tobacco, some of its components or smoking habit had some protective effect, more
females would be expected to be hospitalised, but this was not the case. Some confounding factors
could exist and would condition the number of hospitalised females. What we doubtlessly observed
was that the difference between smokers hospitalised for COVID-19 and the expected values was
very significant. Another interpretation could be that smokers were more likely to catch the disease
from their habits: touching cigarettes and cigarette packets, exchanging tobacco, touching their face
or placing cigarettes in their mouths, etc. Other factors or artefacts could bias this study. For instance,
as smokers know they are an at-risk population, they could have been more aware of taking social
distancing and hygiene measures. Nonetheless, as the time frame within which the studies were
conducted was an early stage of today’s pandemic and no differences were observed among them,
this would not appear to be a plausible hypothesis.
Another possible bias may have something to do with data quality. We believe that smokers
could have attempted to hide this characteristic given the alarm of these characteristics, and the
threat of hospitals and ICUs being overcrowded. Nonetheless, most data were taken from electronic
medical records, which meant that we had access to patients’ smoking background in many cases.
Given the serious nature of the pandemic, in other cases we could presume that many smoking
patients had stopped smoking before being hospitalised and were, thus, included in the groups of
non-smokers or former smokers. So, it would be very interesting to specify the exact time when
these data were collected, for example during a medical interview when admitted to hospital or
from patients’ previous medical records. Moreover, the definition of smoker in such studies is not
clear because heavy smokers are not distinguished from occasional smokers.
In any case, it is necessary to remember that tobacco causes 20,000 deaths a day all over the
world [16] and, with COVID-19 patients, it generally comes with comorbidities, which means a
worse prognosis [15].
4.1. Physiological substrate for anti-inflammatory pulmonary effect
SARS-CoV-2 causes varying degrees of illness. Fever and cough are dominant symptoms, but
severe disease also occurs. When COVID-19 patients’ aggravation takes place, lung
hyperinflammation may appear due to virus-activated “cytokine storm” or CRS [45]. Of the different
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
8 of 12
cytokines that increase and reach such an exacerbated response [46], Interleukin-6 (IL-6) in serum is
mainly expected to predict SARS-CoV-2 pneumonia severity as the suppression of
pro-inflammatory IL-6 has been demonstrated to have a therapeutic effect on many inflammatory
diseases, including viral infections [47]. In severe cases, SARS-CoV-2 has been shown to activate
both innate and adaptive immune systems in alveolar tissue by inducing the release of many
cytokines and subsequent cytokine release syndrome [48]. During this response, levels of
pro-inflammatory cytokines (include TNF, interleukin (IL)-1b, IL-6, and IL-8) rise [46], which is an
important cause of death [49]. Therefore, it is believed that controlling such crucial inflammatory
factors could be a successful approach to reduce mortality in severe patients.
The existence of a cholinergic anti-inflammatory pathway has been demonstrated, which
modulates inflammatory responses during systemic inflammation [50]. 7-nicotinic acetylcholine
receptors (7nAChR) are known to be expressed in macrophages and are essential for attenuating
the inflammatory response by their activation during systemic inflammation [51]. The underlying
mechanism conveys that 7nAChR activation in infiltrated inflammatory cells, including
macrophages and neutrophils, induces not only the suppression of NF-kB activation [52], but also
the secretion of pro-inflammatory cytokines and chemokines from inflammatory cells, including
alveolar macrophages [53]. In lungs, this process involves a physiological feedback mechanism as it
has been demonstrated that pulmonary injury signals produced by inflammation are transmitted by
vagal sensory neurons to the central nervous system [54], where they are integrated and
transformed into a vagal reflex [55]. This response activates the parasympathetic neurons innervated
by the efferent vagus nerve, which results in a higher ACh concentration in lungs [56]. Interestingly,
it has been reported that nicotine, an 7nAChR agonist, exerts an anti-inflammatory effect of acute
lung injury in a murine model [51]. In other inflammatory diseases, such as ulcerative colitis (UC),
smoking or treatment with nicotine has been demonstrated to significantly reduce the risk of
developing the disease [52]. Indeed, nicotine has been shown to reduce acute colonic inflammation
severity with the concomitant inhibition of IL-6 mRNA expression [57–59]. So, nicotine, an
exogenous α7nAchR agonist, has already been demonstrated to selectively down-regulate the
inflammatory response in a number of infection and inflammatory t has also been suggested that
smoking could interact with susceptibility to SARS-CoV-2 infection through the renin-angiotensin
system [60]. It is believed that SARS-CoV-2 uses the angiotensin-converting enzyme-2 (ACE2)
receptor to enter cells [14]. However, while smoking would induce chronic lung damage that would,
in turn, increase susceptibility to severe COVID forms, evidence suggests that nicotine
down-regulates compensatory ACE2 [60,61]. These results support the data included in Table 1 and
could explain why smoking is either harmful or presents an unexpected protective effect by
reducing the virus entry pathway.
5. Conclusions
The number of hospitalised smokers was smaller than expected based on the smoking
prevalence in the different countries. The meta-analysis results obtained in China, US and Italy
indicated that smoking habit lowers the likelihood of being hospitalised by COVID-19.
Currently, the most promising trial under run to treat severe COVID-19 patients is the one
using Tocilizumab, a blocker of IL-6 receptor for the treatment of cytokine storm [47]. However, very
strict criteria for clinical use limits its availability, mainly due to price and adverse effects. Another
recent strategy has proposed the use of Baricitinib, which is predicted to reduce the ability of the
virus to infect lung cells through ACE2 receptor [62], although drugs with similar action mechanism
used in oncology bring serious side-effects [62,63]. Nevertheless, to our knowledge, no clinical trials
of nicotine in COVID-19 patients are currently being run. We suspect that nicotine could be
contribute to an amelioration of the cytokine storm and severe related inflammatory response
through the 7nAChR-mediated cholinergic anti-inflammatory pathway during patient’s
aggravation. Hence, therapeutic strategies probably should consider the combination of antiviral
and anti-inflammatory treatments [64] in order to reduce viral infectivity, viral replication,
exacerbated inflammatory response, and to limit side effects.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
9 of 12
Author Contributions: JGR and AN designed the study and collected data. JGR, LJD, JDNL, and AN wrote the
manuscript. All authors analysed and interpreted the data.
Funding: The University of Castilla-La Mancha Research Programme.
Acknowledgments: The authors thank Dr. Isabel Najera, Dr. Jose Antonio Najera, and Julio Basulto for helpful
comments that greatly improved the manuscript.
Conflicts of Interest: “The authors declare no conflict of interest.”
References
1. Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A Novel
Coronavirus from Patients with Pneumonia in China, 2019. New England Journal of Medicine 2020,
doi:10.1056/NEJMoa2001017.
2. Ksiazek, T.G.; Erdman, D.; Goldsmith, C.S.; Zaki, S.R.; Peret, T.; Emery, S.; Tong, S.X.; Urbani, C.; Comer,
J.A.; Lim, W.; et al. A novel coronavirus associated with severe acute respiratory syndrome. N. Engl. J.
Med. 2003, 348, 1953–1966, doi:10.1056/NEJMoa030781.
3. Cui, J.; Li, F.; Shi, Z.-L. Origin and evolution of pathogenic coronaviruses. Nat. Rev. Microbiol. 2019, 17,
181–192, doi:10.1038/s41579-018-0118-9.
4. Pedersen, S.F.; Ho, Y.-C. SARS-CoV-2: a storm is raging. The Journal of clinical investigation 2020,
doi:10.1172/JCI137647.
5. Lawrence, H.; Hunter, A.; Murray, R.; Lim, W.S.; McKeever, T. Cigarette smoking and the occurrence of
influenza - Systematic review. J. Infect. 2019, 79, 401–406, doi:10.1016/j.jinf.2019.08.014.
6. Alraddadi, B.M.; Watson, J.T.; Almarashi, A.; Abedi, G.R.; Turkistani, A.; Sadran, M.; Housa, A.; Almazroa,
M.A.; Alraihan, N.; Banjar, A.; et al. Risk Factors for Primary Middle East Respiratory Syndrome
Coronavirus Illness in Humans, Saudi Arabia, 2014. Emerging Infect. Dis. 2016, 22, 49–55,
doi:10.3201/eid2201.151340.
7. Cai, H. Sex difference and smoking predisposition in patients with COVID-19. The Lancet. Respiratory
medicine 2020, doi:10.1016/S2213-2600(20)30117-X.
8. Lippi, G.; Henry, B.M. Active smoking is not associated with severity of coronavirus disease 2019
(COVID-19). European journal of internal medicine 2020, doi:10.1016/j.ejim.2020.03.014.
9. Kabbani, N.; Olds, J.L. Does COVID19 infect the brain? If so, smokers might be at a higher risk. Molecular
pharmacology 2020, doi:10.1124/molpharm.120.000014.
10. Berlin, I.; Thomas, D.; Le Faou, A.-L.; Cornuz, J. COVID-19 and smoking. Nicotine & tobacco research :
official journal of the Society for Research on Nicotine and Tobacco 2020, doi:10.1093/ntr/ntaa059.
11. Fu, L.; Wang, B.; Yuan, T.; Chen, X.; Ao, Y.; Fitzpatrick, T.; Li, P.; Zhou, Y.; Lin, Y.; Duan, Q.; et al. Clinical
characteristics of coronavirus disease 2019 (COVID-19) in China: a systematic review and meta-analysis.
The Journal of infection 2020, doi:10.1016/j.jinf.2020.03.041.
12. Zhao, Q.; Meng, M.; Kumar, R.; Wu, Y.; Huang, J.; Lian, N.; Deng, Y.; Lin, S. The impact of COPD and
smoking history on the severity of Covid-19: A systemic review and meta-analysis. Journal of medical
virology 2020, doi:10.1002/jmv.25889.
13. Berlin, I.; Thomas, D.; Le Faou, A.-L.; Cornuz, J. COVID-19 and smoking. Nicotine & tobacco research :
official journal of the Society for Research on Nicotine and Tobacco 2020, doi:10.1093/ntr/ntaa059.
14. Brake, S.J.; Barnsley, K.; Lu, W.; McAlinden, K.D.; Eapen, M.S.; Sohal, S.S. Smoking Upregulates
Angiotensin-Converting Enzyme-2 Receptor: A Potential Adhesion Site for Novel Coronavirus
SARS-CoV-2 (Covid-19). Journal of clinical medicine 2020, 9, doi:10.3390/jcm9030841.
15. Vardavas, C.I.; Nikitara, K. COVID-19 and smoking: A systematic review of the evidence. Tobacco
induced diseases 2020, 18, 20–20, doi:10.18332/tid/119324.
16. WHO | WHO report on the global tobacco epidemic 2019 Available online:
http://www.who.int/tobacco/global_report/en/ (accessed on Apr 20, 2020).
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
10 of 12
17. Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with
Coronavirus Disease 2019 - United States, February 12-March 28, 2020. MMWR. Morbidity and mortality
weekly report 2020, 69, 382–386, doi:10.15585/mmwr.mm6913e2.
18. Goyal, P.; Choi, J.J.; Pinheiro, L.C.; Schenck, E.J.; Chen, R.; Jabri, A.; Satlin, M.J.; Campion, T.R.J.; Nahid, M.;
Ringel, J.B.; et al. Clinical Characteristics of Covid-19 in New York City. The New England journal of
medicine 2020, doi:10.1056/NEJMc2010419.
19. Colombi, D.; Bodini, F.C.; Petrini, M.; Maffi, G.; Morelli, N.; Milanese, G.; Silva, M.; Sverzellati, N.P.;
Michieletti, E. Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19
Pneumonia. Radiology 2020, 201433–201433, doi:10.1148/radiol.2020201433.
20. Guan, W.-J.; Liang, W.-H.; Zhao, Y.; Liang, H.-R.; Chen, Z.-S.; Li, Y.-M.; Liu, X.-Q.; Chen, R.-C.; Tang, C.-L.;
Wang, T.; et al. Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide
Analysis. The European respiratory journal 2020, doi:10.1183/13993003.00547-2020.
21. Liu, W.; Tao, Z.-W.; Lei, W.; Ming-Li, Y.; Kui, L.; Ling, Z.; Shuang, W.; Yan, D.; Jing, L.; Liu, H.-G.; et al.
Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus
disease. Chin. Med. J. 2020, doi:10.1097/CM9.0000000000000775.
22. Liu, S.; Zhang, M.; Yang, L.; Li, Y.; Wang, L.; Huang, Z.; Wang, L.; Chen, Z.; Zhou, M. Prevalence and
patterns of tobacco smoking among Chinese adult men and women: findings of the 2010 national smoking
survey. J. Epidemiol. Community Health 2017, 71, 154–161, doi:10.1136/jech-2016-207805.
23. CDC Current Cigarette Smoking Among Adults in the United States Available online:
https://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/index.htm (accessed on
Apr 22, 2020).
24. Salute, M. della Rapporto 2018 sulla prevenzione e controllo del tabagismo Available online:
http://www.salute.gov.it/portale/documentazione/p6_2_2_1.jsp?lingua=italiano&id=2851 (accessed on
Apr 24, 2020).
25. Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A.;
PRISMA-P Group Preferred reporting items for systematic review and meta-analysis protocols
(PRISMA-P) 2015 statement. Systematic Reviews 2015, 4, 1, doi:10.1186/2046-4053-4-1.
26. Stroup, D.F.; Berlin, J.A.; Morton, S.C.; Olkin, I.; Williamson, G.D.; Rennie, D.; Moher, D.; Becker, B.J.; Sipe,
T.A.; Thacker, S.B. Meta-analysis of observational studies in epidemiology: a proposal for reporting.
Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000, 283, 2008–2012,
doi:10.1001/jama.283.15.2008.
27. Chen, T.; Wu, D.; Chen, H.; Yan, W.; Yang, D.; Chen, G.; Ma, K.; Xu, D.; Yu, H.; Wang, H.; et al. Clinical
characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. Bmj-British
Medical Journal 2020, 368, m1091, doi:10.1136/bmj.m1091.
28. Guan, W.-J.; Ni, Z.-Y.; Hu, Y.; Liang, W.-H.; Ou, C.-Q.; He, J.-X.; Liu, L.; Shan, H.; Lei, C.-L.; Hui, D.S.C.; et
al. Clinical Characteristics of Coronavirus Disease 2019 in China. The New England journal of medicine
2020, doi:10.1056/NEJMoa2002032.
29. Han, X.; Cao, Y.; Jiang, N.; Chen, Y.; Alwalid, O.; Zhang, X.; Gu, J.; Dai, M.; Liu, J.; Zhu, W.; et al. Novel
Coronavirus Pneumonia (COVID-19) Progression Course in 17 Discharged Patients: Comparison of
Clinical and Thin-Section CT Features During Recovery. Clinical infectious diseases : an official
publication of the Infectious Diseases Society of America 2020, doi:10.1093/cid/ciaa271.
30. Jin, X.; Lian, J.-S.; Hu, J.-H.; Gao, J.; Zheng, L.; Zhang, Y.-M.; Hao, S.-R.; Jia, H.-Y.; Cai, H.; Zhang, X.-L.; et
al. Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019
(COVID-19) with gastrointestinal symptoms. Gut 2020, doi:10.1136/gutjnl-2020-320926.
31. Li, X.; Xu, S.; Yu, M.; Wang, K.; Tao, Y.; Zhou, Y.; Shi, J.; Zhou, M.; Wu, B.; Yang, Z.; et al. Risk factors for
severity and mortality in adult COVID-19 inpatients in Wuhan. The Journal of allergy and clinical
immunology 2020, doi:10.1016/j.jaci.2020.04.006.
32. Lian, J.; Jin, X.; Hao, S.; Cai, H.; Zhang, S.; Zheng, L.; Jia, H.; Hu, J.; Gao, J.; Zhang, Y.; et al. Analysis of
Epidemiological and Clinical features in older patients with Corona Virus Disease 2019 (COVID-19) out of
Wuhan. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
2020, doi:10.1093/cid/ciaa242.
33. Mo, P.; Xing, Y.; Xiao, Y.; Deng, L.; Zhao, Q.; Wang, H.; Xiong, Y.; Cheng, Z.; Gao, S.; Liang, K.; et al.
Clinical characteristics of refractory COVID-19 pneumonia in Wuhan, China. Clinical infectious diseases :
an official publication of the Infectious Diseases Society of America 2020, doi:10.1093/cid/ciaa270.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
11 of 12
34. Wan, S.; Xiang, Y.; Fang, W.; Zheng, Y.; Li, B.; Hu, Y.; Lang, C.; Huang, D.; Sun, Q.; Xiong, Y.; et al. Clinical
features and treatment of COVID-19 patients in northeast Chongqing. Journal of Medical Virology 2020,
doi:10.1002/jmv.25783.
35. Wang, R.; Pan, M.; Zhang, X.; Fan, X.; Han, M.; Zhao, F.; Miao, M.; Xu, J.; Guan, M.; Deng, X.; et al.
Epidemiological and clinical features of 125 Hospitalized Patients with COVID-19 in Fuyang, Anhui,
China. International journal of infectious diseases : IJID : official publication of the International Society
for Infectious Diseases 2020, doi:10.1016/j.ijid.2020.03.070.
36. Zhang, J.-J.; Dong, X.; Cao, Y.-Y.; Yuan, Y.-D.; Yang, Y.-B.; Yan, Y.-Q.; Akdis, C.A.; Gao, Y.-D. Clinical
characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy 2020,
doi:10.1111/all.14238.
37. Zhang, R.; Ouyang, H.; Fu, L.; Wang, S.; Han, J.; Huang, K.; Jia, M.; Song, Q.; Fu, Z. CT features of
SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive
patients from Wuhan city. European radiology 2020, doi:10.1007/s00330-020-06854-1.
38. Zhang, X.; Cai, H.; Hu, J.; Lian, J.; Gu, J.; Zhang, S.; Ye, C.; Lu, Y.; Jin, C.; Yu, G.; et al. Epidemiological,
clinical characteristics of cases of SARS-CoV-2 infection with abnormal imaging findings. International
journal of infectious diseases : IJID : official publication of the International Society for Infectious
Diseases 2020, doi:10.1016/j.ijid.2020.03.040.
39. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course
and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort
study. Lancet (London, England) 2020, 395, 1054–1062, doi:10.1016/S0140-6736(20)30566-3.
40. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features
of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506,
doi:10.1016/S0140-6736(20)30183-5.
41. Yao, Q.; Wang, P.; Wang, X.; Qie, G.; Meng, M.; Tong, X.; Bai, X.; Ding, M.; Liu, W.; Liu, K.; et al.
Retrospective study of risk factors for severe SARS-Cov-2 infections in hospitalized adult patients. Polish
archives of internal medicine 2020, doi:10.20452/pamw.15312.
42. Zhang, R.; Ouyang, H.; Fu, L.; Wang, S.; Han, J.; Huang, K.; Jia, M.; Song, Q.; Fu, Z. CT features of
SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive
patients from Wuhan city. European Radiology 2020, doi:10.1007/s00330-020-06854-1.
43. Lian, J.; Jin, X.; Hao, S.; Cai, H.; Zhang, S.; Zheng, L.; Jia, H.; Hu, J.; Gao, J.; Zhang, Y.; et al. Analysis of
Epidemiological and Clinical features in older patients with Corona Virus Disease 2019 (COVID-19) out of
Wuhan. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America
2020, doi:10.1093/cid/ciaa242.
44. Jordan, R.E.; Adab, P.; Cheng, K.K. Covid-19: risk factors for severe disease and death. Bmj-British Medical
Journal 2020, 368, m1198, doi:10.1136/bmj.m1198.
45. Ruan, Q.; Yang, K.; Wang, W.; Jiang, L.; Song, J. Clinical predictors of mortality due to COVID-19 based on
an analysis of data of 150 patients from Wuhan, China. Intensive Care Med 2020,
doi:10.1007/s00134-020-05991-x.
46. Lee, D.W.; Gardner, R.; Porter, D.L.; Louis, C.U.; Ahmed, N.; Jensen, M.; Grupp, S.A.; Mackall, C.L.
Current concepts in the diagnosis and management of cytokine release syndrome. Blood 2014, 124,
188–195, doi:10.1182/blood-2014-05-552729.
47. Zhang, C.; Wu, Z.; Li, J.-W.; Zhao, H.; Wang, G.-Q. The cytokine release syndrome (CRS) of severe
COVID-19 and Interleukin-6 receptor (IL-6R) antagonist Tocilizumab may be the key to reduce the
mortality. Int. J. Antimicrob. Agents 2020, 105954, doi:10.1016/j.ijantimicag.2020.105954.
48. Zhang, W.; Zhao, Y.; Zhang, F.; Wang, Q.; Li, T.; Liu, Z.; Wang, J.; Qin, Y.; Zhang, X.; Yan, X.; et al. The use
of anti-inflammatory drugs in the treatment of people with severe coronavirus disease 2019 (COVID-19):
The experience of clinical immunologists from China. Clinical immunology (Orlando, Fla.) 2020, 214,
108393–108393, doi:10.1016/j.clim.2020.108393.
49. Mehta, P.; McAuley, D.F.; Brown, M.; Sanchez, E.; Tattersall, R.S.; Manson, J.J. COVID-19: consider
cytokine storm syndromes and immunosuppression. The Lancet 2020, 395, 1033–1034,
doi:10.1016/S0140-6736(20)30628-0.
50. Borovikova, L.V.; Ivanova, S.; Zhang, M.; Yang, H.; Botchkina, G.I.; Watkins, L.R.; Wang, H.; Abumrad, N.;
Eaton, J.W.; Tracey, K.J. Vagus nerve stimulation attenuates the systemic inflammatory response to
endotoxin. Nature 2000, 405, 458–462, doi:10.1038/35013070.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
12 of 12
51. Mabley, J.; Gordon, S.; Pacher, P. Nicotine exerts an anti-inflammatory effect in a murine model of acute
lung injury. Inflammation 2011, 34, 231–237, doi:10.1007/s10753-010-9228-x.
52. Gomes, J.P.; Watad, A.; Shoenfeld, Y. Nicotine and autoimmunity: The lotus’ flower in tobacco. Pharmacol.
Res. 2018, 128, 101–109, doi:10.1016/j.phrs.2017.10.005.
53. Bernardo, D.; Vallejo-Díez, S.; Mann, E.R.; Al-Hassi, H.O.; Martínez-Abad, B.; Montalvillo, E.; Tee, C.T.;
Murugananthan, A.U.; Núñez, H.; Peake, S.T.C.; et al. IL-6 promotes immune responses in human
ulcerative colitis and induces a skin-homing phenotype in the dendritic cells and Tcells they stimulate. Eur.
J. Immunol. 2012, 42, 1337–1353, doi:10.1002/eji.201142327.
54. Yu, J.; Lin, S.; Zhang, J.; Otmishi, P.; Guardiola, J.J. Airway nociceptors activated by pro-inflammatory
cytokines. Respiratory Physiology & Neurobiology 2007, 156, 116–119, doi:10.1016/j.resp.2006.11.005.
55. Wine, J.J. Parasympathetic control of airway submucosal glands: Central reflexes and the airway intrinsic
nervous system. Autonomic Neuroscience 2007, 133, 35–54, doi:10.1016/j.autneu.2007.01.008.
56. 5Yamada, M.; Ichinose, M. The cholinergic anti-inflammatory pathway: an innovative treatment strategy
for respiratory diseases and their comorbidities. Curr Opin Pharmacol 2018, 40, 18–25,
doi:10.1016/j.coph.2017.12.003.
57. Hayashi, S.; Hamada, T.; Kadowaki, M. Mo1960 Nicotine Suppresses Colonic Tumorigenesis Associated
With Chronic Colitis in Mice Through the Inhibition of IL-6 and TNF-α Expression. Gastroenterology 2013,
144, S-705, doi:10.1016/S0016-5085(13)62612-8.
58. Sandborn, W.J.; Tremaine, W.J.; Offord, K.P.; Lawson, G.M.; Petersen, B.T.; Batts, K.P.; Croghan, I.T.; Dale,
L.C.; Schroeder, D.R.; Hurt, R.D. Transdermal nicotine for mildly to moderately active ulcerative colitis - A
randomized, double-blind, placebo-controlled trial. Ann. Intern. Med. 1997, 126, 364–371,
doi:10.7326/0003-4819-126-5-199703010-00004.
59. Pullan, R.D.; Rhodes, J.; Ganesh, S.; Mani, V.; Morris, J.S.; Williams, G.T.; Newcombe, R.G.; Russell, M.A.;
Feyerabend, C.; Thomas, G.A. Transdermal nicotine for active ulcerative colitis. N. Engl. J. Med. 1994, 330,
811–815, doi:10.1056/NEJM199403243301202.
60. Alifano, M.; Alifano, P.; Forgez, P.; Iannelli, A. Renin-angiotensin system at the heart of COVID-19
pandemic. Biochimie 2020, doi:10.1016/j.biochi.2020.04.008.
61. Oakes, J.M.; Fuchs, R.M.; Gardner, J.D.; Lazartigues, E.; Yue, X. Nicotine and the renin-angiotensin system.
Am. J. Physiol. Regul. Integr. Comp. Physiol. 2018, 315, R895–R906, doi:10.1152/ajpregu.00099.2018.
62. Richardson, P.; Griffin, I.; Tucker, C.; Smith, D.; Oechsle, O.; Phelan, A.; Stebbing, J. Baricitinib as potential
treatment for 2019-nCoV acute respiratory disease. The Lancet 2020, 395, e30–e31,
doi:10.1016/S0140-6736(20)30304-4.
63. Pu, S.-Y.; Xiao, F.; Schor, S.; Bekerman, E.; Zanini, F.; Barouch-Bentov, R.; Nagamine, C.M.; Einav, S.
Feasibility and biological rationale of repurposing sunitinib and erlotinib for dengue treatment. Antiviral
Res. 2018, 155, 67–75, doi:10.1016/j.antiviral.2018.05.001.
64. Stebbing, J.; Phelan, A.; Griffin, I.; Tucker, C.; Oechsle, O.; Smith, D.; Richardson, P. COVID-19: combining
antiviral and anti-inflammatory treatments. The Lancet Infectious Diseases 2020, 20, 400–402,
doi:10.1016/S1473-3099(20)30132-8.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 April 2020 doi:10.20944/preprints202004.0540.v1
