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What is Happening with Smokers and COVID-19? A Systematic Review and a Meta-Analysis

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Abstract and Figures

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 (WHO), 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.
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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
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© 2020 by the author(s). Distributed under a Creative Commons CC BY license.
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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 [812]. 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 [1315]. 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
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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
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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
[2741], 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]
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 [27,29,31,33,36,3942], 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
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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
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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].
current
smokers
95%CI
expected current
smokers
(male/female)
Sig.
CDC, 2020
35 (1·7%)
[1·2-2·3]
278·6
p<0·0001
Goyal et al., 2020
20 (5·1%)
[2·9-7·3]
55·7 (37·1, 18·6)
p<0·0001
USA, combined
55 (2·3%)
[1·7, 2·9]
334·3
p<0·0001
Colombi et al., 2020
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
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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
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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 [5759]. 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.
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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.”
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... Eight publications [1][2][3][4][5][6][7][8] considered smoking prevalence in hospitalized patients, generally agreeing it was substantially less than expected from national statistics. This evidence does not necessarily show smoking protects against acquiring COVID-19. ...
... The other meta-analyses [3,6,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] concerned hospitalized patients, relating smoking to severity, progression or death from COVID-19, mainly from studies in China. While these generally reported positive associations which were often statistically significant, many meta-analysis estimates were unadjusted even for age, with comorbidities present pre-pandemic rarely considered. ...
... Recent publications, particularly by Farsalinos et al [2][3][4][6][7][8], have observed that the prevalence of smoking seen in the studies of hospitalized patients was substantially less than reported in national statistics by a factor of four or so, and have suggested that smokers might be protected against getting COVID-19. While the mean percentage of current and former smokers in the studies of hospitalized patients that we considered (current 7.76%, SE 1.00%; ever 33.24%, ...
... Earlier meta-analyses aimed to elucidate the effect of smoking on the prevalence and severity of COVID-19 (35,(38)(39)(40)(41)(42)(43). They analysed studies with large differences in cohort sizes and inconsistent endpoints ( (38,43). ...
... They analysed studies with large differences in cohort sizes and inconsistent endpoints ( (38,43). The majority of meta-analyses did not identify statistically significant effects, or only a questionable effect of smoking on the severity of COVID-19 (39)(40)(41). The latter was observed in a meta-analysis of case studies by Zhao et al., who reported for active smokers a doubling of the risk to develop sever COVID-19 compared to non-smokers (weighted odd ratio of about two), which vanished after breaking down and compiling studies according to differences in endpoints (40). ...
... Earlier meta-analyses aimed to elucidate the effect of smoking on the prevalence and severity of COVID-19 (35,(38)(39)(40)(41)(42)(43). They analysed studies with large differences in cohort sizes and inconsistent endpoints ( (38,43). ...
... They analysed studies with large differences in cohort sizes and inconsistent endpoints ( (38,43). The majority of meta-analyses did not identify statistically significant effects, or only a questionable effect of smoking on the severity of COVID-19 (39)(40)(41). The latter was observed in a meta-analysis of case studies by Zhao et al., who reported for active smokers a doubling of the risk to develop sever COVID-19 compared to non-smokers (weighted odd ratio of about two), which vanished after breaking down and compiling studies according to differences in endpoints (40). ...
Technical Report
Full-text available
Prevalence data for smoking and comorbidities (hypertension, diabetes mellitus, and chronic obstructive pulmonary disease) reported in 25 studies, which partially identified a potentially beneficial effect of smoking/nicotine intake, were re-analysed to investigate the relationship between COVID-19 mortality and national smoking prevalence taking account of known risk factors associated with mortality.
... This manuscript was released as Pre-print at https://www.preprints.org/ manuscript/202004.0540/v1 on 2020/04/30 that has not been peer-reviewed [92]. ...
Article
Full-text available
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 the 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 28th April 2020 and a meta-analysis. We included 18 recent COVID-19 clinical and epidemiological studies based on smoking patient status from 720 initial studies in China, the 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). In conclusion, the analysis of data from 18 studies shows a much lower percentage of hospitalised current smokers than expected. As more studies become available, this trend should be checked to obtain conclusive results and to explore, where appropriate, the underlying mechanism of the severe progression and adverse outcomes of COVID-19.
... Fourth, surprisingly, cigarette smokers have been found to be more resistant to coronavirus and the reason for such unusual behaviour is still unknown though it has been suggested that nicotine may lower cytokine storm [49]. Incidentally, nicotine enhances serum PRL [50] and thus can explain the observation. ...
Article
Prolactin (PRL), the well-known lactogenic hormone, plays a crucial role in immune function given the fact that long term hypoprolactinemia (serum prolactin level below normal) can even lead to death from opportunistic infection. High blood PRL level is known to provide an immunological advantage in many pathological conditions (with some exceptions like autoimmune diseases) and women, because of their higher blood PRL level, get an advantage in this regard. It has been reported that by controlled enhancement of blood PRL level (within the physiological limit and in some cases a little elevated above the normal to induce mild hyperprolactinemia) using dopamine antagonists such immune-stimulatory advantage can led to survival of the patients in many critical conditions. Here it is hypothesized that through controlled augmentation of blood PRL level using dopamine antagonists like domperidone/metoclopramide, which are commonly used drugs for the treatment of nausea and vomiting, both innate and adaptive immunity can be boosted to evade or tone down COVID-19. The hypothesis is strengthened from the fact that at least seven little-understood salient observations in coronavirus patients can apparently be explained by considering the role of enhanced PRL in line with the proposed hypothesis and hence, clinical trials (both therapeutic and prophylactic) on the role of enhanced PRL on the course and outcome of coronavirus patients should be conducted accordingly.
... However, it was soon observed that the prevalence of smokers hospitalized with COVID-19 was considerably lower compared to the smoking rate in the general population. This finding was confirmed using data on hospitalized patients in China (Arsalan's et al. 2020a), the United States (Chow et al. 2020;Petrilli et al. 2020), France , and Italy (Gonzalez-Rubio et al. 2020). The difference in the prevalence of smokers hospitalized with COVID-19 is not only consistent across countries but also substantial in its magnitude: for example, Farsalinos et al. (2020a) estimate it to be approximately one-fourth of the expected gender-adjusted smoking prevalence in China. 1 If correct, this regularity would imply a protective effect of smoking against COVID-19 that might be the key to developing an effective therapy for the decease. ...
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Full-text available
A low prevalence of smokers among the confirmed patients with COVID-19 has been reported by multiple hospital-based studies, and this observation gave rise to a hypothesis that smoking has a protective effect against the novel coronavirus. We test this prediction in a population-based study across the US states and use an instrumental variable approach to address the endogeneity of smoking rates. We find that a higher prevalence of smoking has a significant negative effect on the spread and the severity of the COVID-19 pandemic across the US state: it decreases the per capita number of registered cases, the case fatality rate, and the excess mortality. The protective effect is more pronounced in subgroups of the population that are more likely to be smokers: men of all ages and females of the older cohort. Our findings are robust to the inclusion of a broad range of control variables, exclusion of outliers, and placebo tests. Despite the protective effect against the COVID-19, smoking remains detrimental for health in the long-term, and we show that states with a higher rate of smoking also have higher mortality in the year before the outbreak.
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Introduction: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been spread worldwide. Objectives: To identify the clinical characteristics and risk factors associated with the severe incidence of SARS-CoV-2 infection. Patients and methods: All adult patients (≥18 years old) consecutively admitted in Dabieshan Medical Center from January 30, 2020 to February 11, 2020 were collected and reviewed. Only patients diagnosed with COVID-19 according to WHO interim guidance were included in this retrospective cohort study. Results: A total of 108 patients with COVID-19 were retrospectively analyzed. Twenty-five patients (23.1%, 25/108) developed severe disease, and of those 12 (48%, 12/25) patients died. Advanced age, co-morbidities with hypertension, higher blood leukocyte count, neutrophil count, higher sensitive C-reactive protein level, D-dimer level, Acute Physiology and Chronic Health Evaluation Ⅱ (APECHE Ⅱ) score and Sequential Organ Failure Assessment (SOFA) score were associated with greater risk of development of severe COVID-19, and so were lower lymphocyte count and albumin level. Multivariable regression showed increasing odds of severe COVID-19 associated with higher SOFA score (OR 2.450, 1.302-4.608; p = 0.005), and lymphocyte count less than 0.8×109 per L (OR 9.017, 2.808-28.857; p <0.001) on admission. The higher SOFA score (OR 2.402, 1.313-4.395; p = 0.004) on admission was identified as risk factor for in-hospital death. Conclusions: Lymphocytopenia and the higher SOFA score on admission could help clinicians to identify patients with high risk for developing severe COVID-19. More related studies are needed in the future.
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Significant aspects of COVID-19 pandemic remain obscure. Angiotensin converting enzyme 2 (ACE2), a component of the renin-angiotensin system, whose expression dominates on lung alveolar epithelial cells, is the human cell receptor of SARS-CoV-2, the causative agent of COVID-19. We strongly encourage the concept that thorough considerations of receptor-ligand interactions should be kept at the heart of scientific debate on infection. In this idea, the whole renin-angiotensin system has to be evaluated. We hypothesize that factors related to ethnicity, environment, behaviors, associated illness, and medications involving this complex system are probably responsible for situations regarded as anomalous from both an epidemiological and a clinical point of view, but, taken together, such factors may explain most of the aspects of current outbreak. We decided to use the analogy of a play and speculate about the possible impact in this tragedy of 1) air pollution via the interference of nitrogen dioxide on ACE2 expression; 2) the dual role of nicotine; 3) the hypothetical involvement of ACE2 polymorphisms, the relationships of which with ethnic factors and susceptibility to cardiovascular disease seems intriguing; 4) the impact on the severity of infection of hypertension and related medications acting on the renin/angiotensin system, and, finally, 5) the possible helpful role of chloroquine, thanks to its capacity of modifying ACE2 affinity to the viral spike protein by altering glycosylation. This hypothesis paper is an urgent call for the development of research programs that aim at questioning whether the putative protagonists of this tragedy are real-life actors in COVID-19.
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Aims Comorbidities are associated with the severity of Coronavirus Disease 2019 (Covid‐19). This meta‐analysis aimed to explore the risk of severe Covid‐19 in patients with pre‐existing chronic obstructive pulmonary disease (COPD) and ongoing smoking history. Methods A comprehensive systematic literature search was carried out to find studies published from December 2019 to 22nd March 2020 from 5 Database. The language of literature included English and Chinese. The point prevalence of severe Covid‐19 in patients with pre‐existing COPD and those with ongoing smoking was evaluated with this meta‐analysis. Results Overall 11 case‐series, published either in Chinese or English language with a total of 2002 cases were included in the study. The pooled OR of COPD and the development of severe Covid‐19 was 4.38 (Fixed effect model, 95% CI: 2.34‐8.20), while the OR of ongoing smoking was 1.98 (Fixed effect model, 95% CI: 1.29‐3.05). There was no publication bias as examined by the funnel plot and Egger's test (p=NS). The heterogeneity of included studies was moderate for both COPD and ongoing smoking history on the severity of Covid‐19. Conclusions COPD and ongoing smoking history attribute to the worse progression and outcome of Covid‐19. This article is protected by copyright. All rights reserved.
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Objective To investigate the epidemiological and clinical features of patients with COVID-19 in Anhui province of China. Method In this descriptive study, we obtained epidemiological, demographic, manifestations, laboratory data and radiological findings of patients confirmed by real-time RT-PCR in the NO.2 People’s Hospital of Fuyang City from Jan 20 to Feb 9, 2020. Clinical outcomes were followed up to Feb 18, 2020. Results Of 125 patients infected SARS-CoV-2, the mean age was 38.76 years (SD, 13.799) and 71(56.8%) were male. Common symptoms include fever [116 (92.8%)], cough [102(81.6%)], and shortness of breath [57(45.6%)]. Lymphocytopenia developed in 48(38.4%) patients. 100(80.0%) patients showed bilateral pneumonia, 26(20.8%) patients showed multiple mottling and ground-glass opacity. All patients were given antiviral therapy. 19(15.2%) patients were transferred to the intensive care unit. By February 18, 47(37.6%) patients were discharged and none of patients died. Among the discharged patients, the median time of length of stay was 14.8 days (SD 4.16). Conclusion In this single-center, retrospective, descriptive study, fever is the most common symptom. Old age, chronic underlying diseases and smoking history may be risk factors to worse condition. Certain laboratory inspection may contribute to the judgment of the severity of illness.
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Objective To better inform efforts to treat and control the current outbreak with a comprehensive characterization of COVID-19. Methods We searched PubMed, EMBASE, Web of Science, and CNKI (Chinese Database) for studies published as of March 2, 2020, and we searched references of identified articles. Studies were reviewed for methodological quality. A random-effects model was used to pool results. Heterogeneity was assessed using I². Publication bias was assessed using Egger's test. Results 43 studies involving 3600 patients were included. Among COVID-19 patients, fever (83.3% [95% CI 78.4–87.7]), cough (60.3% [54.2–66.3]), and fatigue (38.0% [29.8–46.5]) were the most common clinical symptoms. The most common laboratory abnormalities were elevated C-reactive protein (68.6% [58.2–78.2]), decreased lymphocyte count (57.4% [44.8–69.5]) and increased lactate dehydrogenase (51.6% [31.4–71.6]). Ground-glass opacities (80.0% [67.3–90.4]) and bilateral pneumonia (73.2% [63.4–82.1]) were the most frequently reported findings on computed tomography. The overall estimated proportion of severe cases and case-fatality rate (CFR) was 25.6% (17.4–34.9) and 3.6% (1.1–7.2), respectively. CFR and laboratory abnormalities were higher in severe cases, patients from Wuhan, and older patients, but CFR did not differ by gender. Conclusions The majority of COVID-19 cases are symptomatic with a moderate CFR. Patients living in Wuhan, older patients, and those with medical comorbidities tend to have more severe clinical symptoms and higher CFR.
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COVID19 is a devastating global pandemic with epicenters in China, Italy, Spain, and now the United States. While the majority of infected cases appear mild, in some cases individuals present serious cardiorespiratory complications with possible long-term lung damage. Infected individuals report a range of symptoms from headaches to shortness of breath to taste and smell loss. To that end, less is known about the how the virus may impact different organ systems. The SARS-CoV2 virus, which is responsible for COVID19, is highly similar to SARS-CoV. Both viruses have evolved an ability to enter host cells through direct interaction with the angiotensin converting enzyme 2 (ACE2) protein at the surface of many cells. Published findings indicate that SARS-CoV can enter the human nervous system with evidence from both postmortem brains and detection in cerebrospinal fluid of infected individuals. Here we consider the ability of SARS-CoV2 to enter and infect the human nervous system based on the strong expression of the ACE2 target throughout the brain. Moreover, we predict that nicotine exposure through various kinds of smoking (cigarettes, e-cigarettes, or vape) can increase the risk for COVID19 neuroinfection based on known functional interactions between the nicotinic receptor and ACE2. We advocate for higher surveillance and analysis of neuro-complications in infected cases.
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Background In December 2019, COVID-19 outbreak occurred in Wuhan. Data on the clinical characteristics and outcomes of patients with severe COVID-19 are limited. Objective The severity on admission, complications, treatment, and outcomes of COVID-19 patients were evaluated. Methods Patients with COVID-19 admitted to Tongji Hospital from January 26, 2020 to February 5, 2020 were retrospectively enrolled and followed-up until March 3, 2020. Potential risk factors for severe COVID-19 were analyzed by a multivariable binary logistic model. Cox proportional hazard regression model was used for survival analysis in severe patients. Results We identified 269 (49.1%) of 548 patients as severe cases on admission. Elder age, underlying hypertension, high cytokine levels (IL-2R, IL-6, IL-10, and TNF-a), and high LDH level were significantly associated with severe COVID-19 on admission. The prevalence of asthma in COVID-19 patients was 0.9%, markedly lower than that in the adult population of Wuhan. The estimated mortality was 1.1% in nonsevere patients and 32.5% in severe cases during the average 32 days of follow-up period. Survival analysis revealed that male, elder age, leukocytosis, high LDH level, cardiac injury, hyperglycemia, and high-dose corticosteroid use were associated with death in patients with severe COVID-19. Conclusions Patients with elder age, hypertension, and high LDH level need careful observation and early intervention to prevent the potential development of severe COVID-19. Severe male patients with heart injury, hyperglycemia, and high-dose corticosteroid use may have high risk of death.
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Objectives To characterize the chest computed tomography (CT) findings of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) according to clinical severity. We compared the CT features of common cases and severe cases, symptomatic patients and asymptomatic patients, and febrile and afebrile patients.Methods This was a retrospective analysis of the clinical and thoracic CT features of 120 consecutive patients with confirmed SARS-CoV-2 pneumonia admitted to a tertiary university hospital between January 10 and February 10, 2020, in Wuhan city, China.ResultsOn admission, the patients generally complained of fever, cough, shortness of breath, and myalgia or fatigue, with diarrhea often present in severe cases. Severe patients were 20 years older on average and had comorbidities and an elevated lactate dehydrogenase (LDH) level. There were no differences in the CT findings between asymptomatic and symptomatic common type patients or between afebrile and febrile patients, defined according to Chinese National Health Commission guidelines.Conclusions The clinical and CT features at admission may enable clinicians to promptly evaluate the prognosis of patients with SARS-CoV-2 pneumonia. Clinicians should be aware that clinically silent cases may present with CT features similar to those of symptomatic common patients.Key Points • The clinical features and predominant patterns of abnormalities on CT for asymptomatic, typic common, and severe cases were summarized. These findings may help clinicians to identify severe patients quickly at admission. • Clinicians should be cautious that CT findings of afebrile/asymptomatic patients are not better than the findings of other types of patients. These patients should also be quarantined. • The use of chest CT as the main screening method in epidemic areas is recommended.