Statin Therapy and COVID-19 Infection (STACOV PROJECT)
Conditions: COVID, Statin, Cardiovascular Diseases
Sponsor: Institut Investigacio Sanitaria Pere Virgili

STATIN THERAPY AND COVID-19 INFECTION (STACOV PROJECT) 
 
Background: 
 
Statins reduce intracellular cholesterol synthesis by interfering with the limiting enzyme 
HMGCoA reductase. A lower intracellular cholesterol concentration leads to activation 
of the transcription factor SREBP 2 upregulating LDL receptor synthesis. In general, 
intracellular cholesterol homeostasis achieves a new physiological equilibrium at lower 
cholesterol concentrations. Moreover, the effect of cholesterol pathway inhibition has 
also an effect on farnesyl and geranyl molecules formation influencing protein 
prenylation leading to changes on inflammation and immunomodulation in vitro.  
Changes in intracellular cholesterol alter cell membrane composition, particularly the 
structures referred to cholesterol rafts that accommodate a huge number of cell surface 
proteins as receptors. Theoretically, alterations in cholesterol rafts could derange the 
function of some receptors Some preliminary studies on cell models have suggested that 
statins could interfere the activity of some membrane viral receptors blunting its entry to 
cell (Berraondo P et al CIMA nonpublished data). SARS-cov-2 goes into cells through 
the Angiotensin Converser Enzyme 2 (ACE2) which is located in the surface of several 
cells including lung cells. It has been suggested that simvastatin could have a role in 
SARS-cov-2 infection by blocking the virus entry to cell. However, atorvastatin has 
been shown to increase ACE2 expression in animal models [1]. Moreover, intracellular 
cholesterol content seems to influence the virus uptake [2] 
Severe SARS-cov-2 infection is mediated by an inflammatory storm resulting in a deep 
tissue injury, endothelial damage, prothrombotic state and multiorgan failure. As 
mentioned above, statins also have some potent anti-inflammatory effects as modulating 
TNF, the NFkB transcription factor or blocking some members of the Tool Like 
Receptor family as TLR4-9 and its downstream cofactor MYD88 [3][4]. This anti-
inflammatory effect has been implicated in a better prognosis of some diseases as HBV 
or HCV chronic infection, limiting the progression of hepatic damage to chronic liver 
disease or hepatocarcinoma [5].  The impact of statin use on influenza epidemics has 
been repetitively assessed but contradictory or non-conclusive results have been 
obtained [6][7]. The combination of statins and angiotensin receptor blockers have 
shown an important protective effect on other epidemics as Ebola, probably to their 
action on endothelium protection [8].  A protective effect of statins on pulmonary 

hypertension development in a primate HIV model has also been reported [9]. 
Although, in general all these pleiotropic effects of statins have been shown in vitro and 
its clinical impact is not clear, a clinical assay to test the efficacy of simvastatin on 
SARS-cov-2 is going on. Recently an observational study including more than 8000 
patients infected by Sars-Cov-2 showed the protective effect of being on statins or ACE 
inhibitors [10]. Taken into account its widespread use and putative effects on viral 
entry, inflammation, immune mechanisms and endothelial function, the use of standard 
therapies as statins have been postulated to target the host response to new virus 
pandemics [8][11]. 
 
Hypothesis: 
 
Considering that simvastatin, and probably statins in general, interfere with SARS-cov-
2 cellular uptake and some inflammatory pathways activated by the virus, those patients 
on statin therapy should be less vulnerable to infection and their clinical course and 
prognosis should be better than that in individuals not on statin therapy. 
 
Primary objective: 
 
Assess the difference in the WHO SARS-cov-2 scale of severity (9 steps) achieved by 
Covid-19 infected patients, admitted in the hospital, with and without background statin 
therapy comparable in age and gender distribution. 
 
Secondary objectives: 
 
• Compare in Covid-19 infected patients with/without statins the number of patients 
developing pneumonia. 
 
• Compare in Covid-19 infected patients with/without statins the number of patients 
requiring non-invasive respiratory support. 
 
• Compare in Covid-19 infected patients with/without statins the number of patients 
requiring invasive respiratory support. 
 

• Compare in Covid-19 infected patients with/without statins the number of patients 
developing hyperinflammatory syndrome (Ferritin > 800; DD> 1500; CRP> 10; or 
IL-6 > 40) 
 
• Compare in Covid-19 infected patients with/without statins the number of patients 
developing multiorgan failure. 
 
• Compare in Covid-19 infected patients with/without statins the number of deaths 
due to the disease. 
 
• Compare in Covid-19 infected patients with/without statins the mean hospital stays 
of those discharged. 
 
• Impact of lipid profile (last available) on the previous variables. 
 
Methods 
 
This is a retrospective observational multicentre study based on clinical records review. 
The following centres will take part in the study: 
 
LIPIDCAS Group: 
• Hospital Universitari Sant Joan 
• Hospital Universitari Joan XXIII 
• Hospital Sant Pau i Santa Tecla 
• Hospital Verge de la Cinta 
• Pius Hospital de Valls 
• Hospital del Vendrell 
 
Other centres from the LIPID AND ARTERIOSCLEROSIS UNITS NET (XULA) from 
Catalonia.  
 
Inclusion criteria:  
Patients  ≥ 18 years old with a PCR or immunological confirmation of Covid-19 
infection, admitted in the hospital for at least 24 hours, will be included.  
 
Exclusion criteria:  
Patients < 18 years old. 

 
 
Procedures in data collection: 
A database (excel) has been developed for the collection of data common to all 
participating centres. Patient data will be anonymized. Each centre will identify its 
patient with its own code of the centre, for example, the code of Reus will be number 3 
and first patient included will be the 001 and so on successively (3-001,3-002, etc.). 
Once all centres have completed the review, the databases will be merged. The data will 
be stored on the server of the website of the Lipid Units of Southern Catalonia 
(LípidCas) shared by all participating centres and hosted on the server of the Hospital 
Universitari Sant Joan de Reus. 
 
Confidentiality 
The principal investigator undertakes that Hospitals and researchers are responsible for 
ensuring that confidentiality regarding the identification and data of the participant is 
maintained at all times. The name and details that will identify the patient only appear 
in the medical history. Researchers use identification codes without knowing the name 
of the person to whom the sample belongs. Researchers use identification codes without 
knowing the name of the person to whom the sample belongs. The data will be obtained 
from the histories of patients in the different centers and entered into a database 
anonymously. Once the data is entered, they are completely dissociated from the source. 
The fully anonymized database will remain on a server (HUSJ) from which will access 
the people who will work with the data to harmonize the database and perform 
statistical studies that will be performed by staff outside the health institutions (IISPV- 
URV) that will not be in contact with the original source nor will there be traceability to 
identify the source of the data.These procedures are subject to the provisions of Organic 
Law 3/2018 of 5 December on the protection of personal data and Royal Decree 
1720/2007, of 21 December (data protection regulations). However, the provisions of 
Regulation (EU) 2016/6799 on physical protection with regard to the processing of 
personal data will be observed. The personal information collected for the study will be 
identified with a code, in accordance with the procedure established in the Personal 
Data Protection Act. These types of data are called dissociated data. 
 
Variables to be recorded:  

 
• Basic anthropometry and clinical antecedents including comorbidities will be 
recorded.  
• Biochemical, including the last available lipid profile, and clinical variables 
including WHO SARS-cov-2 disease stages and other data associated to Covid-19 
infection will be recorded according a preestablished data base  
• Detailed information about baseline therapies will be also collected.  
• A common data base to introduce information will be prepared, consensual and 
distributed to all centres. (Annex - pending).  
 
Statistical analyses  
 
The main objective is a reduction on WHO Sars-cov-2 index (0-8), calculating a mean 
score in hospital admitted patients of 4,6 (SD:1,12) a 30% reduction on categories 5 to 8 
would result in a mean value of 4.2 (SD:  1.02).  Accepting a relation between statin and 
non-statin group of 0.3 an alpha risk of 0.1 and a beta risk of less than 0.2 in a bilateral 
contrast, 216 subjects are needed in the non-statin group and 109 in the statin group to 
detect a difference equal to or greater than 0.4 units. The common standard deviation is 
assumed to be 1.07. Taking into account an estimated dropout rate of 10% we aim to 
include 250 patients in the non-statin group and 120 in the statin group 
Continuous variables will be tested for normality using the Kolmogorov-Smirnov test. 
Data are presented as means and percentiles 25 and 75 for continuous variables not 
normally distributed or the mean and standard deviation (SD) when distributed 
normally. Unless otherwise noted, categorical variables are expressed as percentages. 
Differences between groups will be analyzed using the non-parametric Mann-Whitney 
test or the Student's parametric t test for continuous variables and the chi-square test or 
Fisher's exact test for categorical variables. Adjusted differences will be investigated by 
covariance analysis (ANCOVA). The Pearson correlation test for parametric variables 
or Spearman for nonparametric variables will be performed to analyze the association 
between continuous variables. 
 
Multivariate analyzes will be performed to evaluate the impact of statins on the severity 
of Sars-cov-2 infection adjusted for the major confounding covariates. These analyzes 
will be of two types, on the one hand, regularized linear regressions with cross-

validation to model the severity of the infection continuously, using the score from 0 to 
8. In addition, survival analyzes will be performed using models of Cox and 
competitive risks in order to measure the relative risk of different infection outcomes 
(discharge, death). Statistical analyzes will be carried out with the SPSS 25 package 
(IBM, Madrid Spain) and the R 3.6 package. A p value less than 0.05 will be considered 
statistically significant. 
 
Expected results  
 
We aim to obtain a statistically significant lower WHO severity index in those Sars-cov-
2 patients admitted to hospital. This difference should be maintained after adjusting for 
confounding covariates. Statin use should be identified by multivariate analyses as a 
protective factor independently of cofactors.   
 
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