# Abnormal liver tests and non-alcoholic fatty liver disease predict disease progression and outcome of patients with Covid-19

Simona Tripon, Pascal Bilbault, Thibaut Fabacher, Nicolas Lefebvre, Sylvain Lescuyer, Emmanuel Andres, Elise Schmitt, Sabrina Garnier-Kepka, Pierrick Le Borgne, Joris Muller, et al.

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## To cite this version:

Simona Tripon, Pascal Bilbault, Thibaut Fabacher, Nicolas Lefebvre, Sylvain Lescuyer, et al. Abnormal liver tests and non-alcoholic fatty liver disease predict disease progression and outcome of patients with Covid-19. Clinics and Research in Hepatology and Gastroenterology, 2022, 46 (5), pp.101894. 10.1016/j.clinre.2022.101894. hal-05465054

HAL Id: hal-05465054

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<table><tr><td>Manuscript Number:</td><td>CLINRE-D-21-01108R2</td></tr><tr><td>Article Type:</td><td>Original article</td></tr><tr><td>Corresponding Author:</td><td>Simona TRIPON<br/>Nouvel Hopital Civil, Hôpitaux Universitaires de Strasbourg<br/>Strasbourg, FRANCE</td></tr><tr><td>First Author:</td><td>Simona TRIPON</td></tr><tr><td>Order of Authors:</td><td>Simona TRIPON<br/>Pascal BILBAULT<br/>Thibaut FABACHER<br/>Nicolas LEFEBVRE<br/>Sylvain LESCUYER<br/>Emmanuel ANDRES<br/>Elise SCHMITT<br/>Sabrina GARNIER-KEPKA<br/>Pierrick LE BORGNE<br/>Joris MULLER<br/>Hamid MERDJI<br/>FREDERIC CHAFFRAIX<br/>Didier MUTTER<br/>Thomas BAUMERT<br/>Ferhat MEZIANI<br/>Michel DOFFOEL</td></tr><tr><td>Abstract:</td><td>Background and aims<br/>Coronavirus disease 2019 (COVID-19) is a serious public health issue that became rapidly pandemic. Liver injury and comorbidities, including metabolic syndrome, are associated with severe forms of the disease. This study sought to investigate liver injury, clinical features, and risk factors in patients with mild, moderate, and severe COVID-19.<br/>Methods<br/>We retrospectively included all consecutive patients hospitalized with laboratory-confirmed COVID-19 between February, 22 and May 15, 2020 at the emergency rooms of a French tertiary hospital. Medical history, symptoms, biological and imaging data were collected.<br/>Results<br/>Among the 1,381 hospitalizations for COVID-19, 719 patients underwent liver tests on admission and 496 (68.9%) patients displayed abnormal liver tests. Aspartate aminotransferase was most commonly abnormal in 57% of cases, followed by gamma-glutamyl transferase, alanine aminotransferase, albumin, alkaline phosphatase, and total bilirubin in 56.5%, 35.9%, 18.4%, 11.4%, and 5.8%. The presence of hepatocellular type more than 2xULN was associated with a higher risk of</td></tr></table>
<table><tr><td></td><td>hospitalization and a worse course of severe disease (odd ratio [OR] 5.499; 95%CI: 1.27-23.86; OR 3.404; 95% CI: 2.12-5.47; p &lt;0.001 respectively). A higher NAFLD fibrosis score was associated with a higher risk of hospitalization (OR 1.754; 95%CI: 1.27-2.43, p&lt;0.001). Patients with high fibrosis-4 index had a 5-fold greater risk of moderate (p &lt;0.001) and severe disease (p &lt;0.001).<br/><br/>Conclusion<br/><br/>Abnormal liver tests are common in patients with COVID-19 and could predict the outcome. Patients with non-alcoholic fatty liver disease and liver fibrosis are at higher risk of progressing to severe COVID-19.</td></tr><tr><td>Suggested Reviewers:</td><td>Raluca PAIS<br/>Raluca.Pais@aphp.fr<br/><br/>Yvon CALMUS<br/>yvon.calmus@aphp.fr</td></tr><tr><td>Opposed Reviewers:</td><td></td></tr><tr><td>Response to Reviewers:</td><td>Reviewer #1: I appreciate the authors efforts to respond to reviewers comment. Still I have some points that should be revised:<br/><br/>1. Page 7 line 6: "This HSI has been validated in COVID-19 patients to evaluate NAFLD ref 8-10" - please rephrase as these publications only used HSI to diagnose NAFLD (because no availability of other noninvasive methods to assign the diagnosis of NAFLD) but have no intention to validate it ...<br/><br/>Modified in the text<br/><br/>2. As suggested in my first review I think that it would be necessary to add a table 1 with all patients' characteristics before splitting patients in different groups according to the severity of COVID 19. This will allow to present some clinical characteristics - mean age, % of diabetes, obesity, HBP etc, as text and other like mean values of LFT and biological parameters in table<br/><br/>As the reviewer suggested in the first review « "Characteristics of COVID-19 patients at baseline" - this paragraph can be shortened; instead of presenting the results for different parameters as text, the authors can add a table (ex Table 1: patient's general characteristics at baseline) with general characteristics of all patients, we shortened the text and we keep only the information that could not be presented in tables. Clinical and biological data such as (mean age, gender, % of diabetes, obesity, hypertension, etc LFTs and date are presented already in tables 1 and 3 for each severity group of patients.<br/><br/>We included in the text the clinical characteristics for the patients as the reviewer suggested.<br/><br/>3. Respectfully, I do not agree with the authors answer "We presented the data in two separated tables because the primary and secondary endpoints were to define the impact of different factors (LFTs, NAFLD, the severity of liver fibrosis, etc) to the risk of hospitalization in conventional Units or ICU. We considered that making two tables allowed us to have a clearer response to these questions comparing easier 2 by 2 groups » . 3 groups comparison can be made using ANOVA. Besides the groups presented in table 1 and 4 does not allow a 2 by 2 groups comparisons as in table 1 we have mild vs moderate and in table 3 mild and moderate are collapsed and compared with the severe group ...<br/><br/>We presented two tables for several reasons:<br/>- the first table is in relation with the main outcome and the second with secondary outcome<br/>- it is easier to represent and understand data when the grouping variable has only two modalities<br/>- we could not do a "ANOVA" here because the populations used are not the same.</td></tr></table>
4. The authors 'comment concerning the collinearity between GGT and non-invasives score of fibrosis/NAFLD used in multivariable models is not acceptable. "We agree with the reviewer that it could be a collinearity, but the endpoints of our study were to show that abnormal liver tests could predict the outcome and patients with nonalcoholic fatty liver disease and liver fibrosis are at higher risk of progressing to severe COVID-19. »

In non-invasives score of fibrosis/NAFLD, "GGT" is not included in models, neither in the FIB-4 index, nor the NAFLD fibrosis score.

5. Need to revise the English

The manuscript was revised by "Cremer Consulting" SARL.
Highlights

1) Abnormal liver tests at admission are associated with a poorer clinical outcome.

2) Patients with metabolic syndrome and NAFLD related fibrosis have a higher risk of hospitalisation.

3) The FIB-4 score at admission may predict the risk to develop a moderate or severe disease.
STROBE Statement—checklist of items that should be included in reports of observational studies

<table><thead><tr><th></th><th>Item No</th><th>Recommendation</th></tr></thead><tbody><tr><td>Title and abstract</td><td>1</td><td>(a) Indicate the study's design with a commonly used term in the title or the abstract<br>(b) Provide in the abstract an informative and balanced summary of what was done and what was found</td></tr><tr><td>Introduction</td><td></td><td></td></tr><tr><td>Background/rationale</td><td>2</td><td>Explain the scientific background and rationale for the investigation being reported</td></tr><tr><td>Objectives</td><td>3</td><td>State specific objectives, including any prespecified hypotheses</td></tr><tr><td>Methods</td><td></td><td></td></tr><tr><td>Study design</td><td>4</td><td>Present key elements of study design early in the paper</td></tr><tr><td>Setting</td><td>5</td><td>Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection</td></tr><tr><td>Participants</td><td>6</td><td>(a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up<br>Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls<br>Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants<br>(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed<br>Case-control study—For matched studies, give matching criteria and the number of controls per case</td></tr><tr><td>Variables</td><td>7</td><td>Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable</td></tr><tr><td>Data sources/ measurement</td><td>8*</td><td>For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group</td></tr><tr><td>Bias</td><td>9</td><td>Describe any efforts to address potential sources of bias</td></tr><tr><td>Study size</td><td>10</td><td>Explain how the study size was arrived at</td></tr><tr><td>Quantitative variables</td><td>11</td><td>Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why</td></tr><tr><td>Statistical methods</td><td>12</td><td>(a) Describe all statistical methods, including those used to control for confounding<br>(b) Describe any methods used to examine subgroups and interactions<br>(c) Explain how missing data were addressed<br>(d) Cohort study—If applicable, explain how loss to follow-up was addressed<br>Case-control study—If applicable, explain how matching of cases and controls was addressed<br>Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy<br>(e) Describe any sensitivity analyses</td></tr></tbody></table>

Continued on next page
## Results

<table><tr><td>Participants</td><td>13*</td><td>(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed<br>(b) Give reasons for non-participation at each stage<br>(c) Consider use of a flow diagram</td></tr><tr><td>Descriptive data</td><td>14*</td><td>(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders<br>(b) Indicate number of participants with missing data for each variable of interest<br>(c) Cohort study—Summarise follow-up time (eg, average and total amount)</td></tr><tr><td>Outcome data</td><td>15*</td><td>Cohort study—Report numbers of outcome events or summary measures over time<br>Case-control study—Report numbers in each exposure category, or summary measures of exposure<br>Cross-sectional study—Report numbers of outcome events or summary measures</td></tr><tr><td>Main results</td><td>16</td><td>(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included<br>(b) Report category boundaries when continuous variables were categorized<br>(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period</td></tr><tr><td>Other analyses</td><td>17</td><td>Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses</td></tr></table>

## Discussion

<table><tr><td>Key results</td><td>18</td><td>Summarise key results with reference to study objectives</td></tr><tr><td>Limitations</td><td>19</td><td>Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias</td></tr><tr><td>Interpretation</td><td>20</td><td>Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence</td></tr><tr><td>Generalisability</td><td>21</td><td>Discuss the generalisability (external validity) of the study results</td></tr></table>

## Other information

<table><tr><td>Funding</td><td>22</td><td>Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based</td></tr></table>

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

**Note:** An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.
All authors declared to have no conflict of interest.

**Funding:**

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
- Although it has been used by several publications to define NAFLD in patients with COVID 19, HIS index has not been validated in COVID 19 patients. Moreover, the use of HIS index is the major limits of the above mentioned publications, as it uses the transaminases levels which are often elevated in COVID19 patients without necessarily having NAFLD. The authors should acknowledge this limitation in the discussion section.

### The corrections are made in the manuscript

- NAFLD fibrosis score is used to stage fibrosis in NAFLD but not to diagnose NAFLD - page 6 line 58

### The corrections are made in the manuscript

- The information in this paragraph is redundant: "The fibrosis was evaluated using fibrosis-4 index (FIB-4): low (<1.30), intermediate (1.30-2.67), and high (>2.67) (11)" and "To evaluate the metabolic liver disease's severity, we determined the FIB-4 scores in patients diagnosed with NAFLD according to the HSI score". Please rephrase.

### The corrections are made in the manuscript

- If thoracic CT scan was available for all patients, didn't you have information about the liver steatosis ?

> In our center, the radiologists performed a « low-dose » CT scan Protocol for patients admitted for SARS Cov 2 infection. Unfortunately, this protocol do not allowed us to assess retrospectively the liver HU in order to evaluate the steatosis.

- Exclusion criteria should be presented more in detail:

We presented more details of the exclusion criteria in the text.

The authors said that they excluded patients with underlying chronic liver disease. Therefore, should we understand that patients with previously known NAFLD were excluded? How do you assess alcohol consumption? (page 6 line 47 - 53).

Patients with confirmed NAFLD (history of NAFLD "follow up") were included in the study. The alcohol consumption was assessed according to EASL guidelines. Data was collected in the anamnesis made in ER or during hospitalization.

- Results section:

"Characteristics of COVID-19 patients at baseline" - this paragraph can be shortened; instead of presenting the results for different parameters as text, the authors can add a table (ex Table 1: patient's general characteristics at baseline) with general characteristics of all patients, eventually split between man and women.
As suggested the reviewer, we shortened this paragraph. The data was presented in the tables 1 et 3.

* I don't understand why the authors presented separately patients with mild vs. moderate and than mild/moderate vs severe instead of performing multiple comparisons (ANOVA) between the 3 groups? I suggest the results section "Comparison between mild and moderate COVID-19 on admission" and "Comparison between mild/moderate and severe COVID-19" be collapsed into a single section " characteristics of COVID 19 patients according to disease severity". The same for the tables 1 & 4 that can be collapsed

We presented the data in two separated tables because the primary and secondary endpoints were to define the impact of different factors (LFTs, NAFLD, the severity of liver fibrosis, etc) to the risk of hospitalization in conventionnel Units or ICU. We considered that making two tables allowed us to have a clearer response to these questions comparing easier 2 by 2 groups.

* I'm not sure about the utility of tables 2 & 5 ...

We considered the reviewer suggestion concerning the tables 2 and 5.

* Authors used HIS to calculate NAFLD. However, NAFLD status it is not shown in tables 1 and 4 describing patients characteristics according to COVID 19 severity. Which was the prevalence of NAFLD (according to HSI) in each disease severity category?

The prevalence of NAFLD is presented in tables 1 and 4 for each category of patients as suggested by the reviewer.

* Which was the interest of using 2 noninvasive fibrosis scores - NAFLD fibrosis score and FIB-4?

We agree with the reviewer commentary there are 2 noninvasive tests of fibrosis scores. We performed 2 types of noninvasive tests to more evidence our results and show that patients with NASH fibrosis had a poorer prognosis. With NAFLD fibrosis score we showed that patients with higher NFS score had a higher risk of hospitalization and a higher risk to develop a severe disease. With the different « cut-off » of FIB-4 that defined the severity of the fibrosis (as defined in the literature) we could estimate that patients with more severe fibroses had 2- and 4-fold time risk to develop a severe COVID-19. Moreover, patients with confirmed steatosis by HIS index who had a low FIB-4 score developed a mild disease.
* For the multivariable models: the authors put into the models the transaminases and GGT but also the FIB4, NFS and NAFLD (diagnosed with HIS) - which all include transaminases in their calculation formula, so there is probably some collinearity in these analysis

We agree with the reviewer that it could be a collinearity, but the endpoints of our study were to show that abnormal liver tests could predict the outcome and patients with non-alcoholic fatty liver disease and liver fibrosis are at higher risk of progressing to severe COVID-19.

* I am surprised that the metabolic risk factors, which are known determinants of COVID 19 severity has not been included into the multivariable models.

There are many limiting factors in this pandemic period. The tests were performed at admission before any drug administration that might disturb the liver tests.

Patients admitted were not fasting and those with mild disease were discharged from the hospital in less than 24h after admission. Patients admitted had drugs administration with glucose infusion that could biasing the results. In the pandemic period with high affluence of patients at ER, it was not possible to assess the abdominal perimeter.

Dyslipidemia, hypertension, and diabetes were considered in patients already diagnosed diseases who had were under treatment.

In multivariate analyze, we included in our tables only the factors statistical significative.

We performed different multivariate analyses, after adjusting for confounding factors for the metabolic factors, but the results were not clinical significative.

For moderate disease: Dyslipidemia OR 1.67; 95%CI: 0.57-4.9; p= 0.348; Hypertension OR 0.844; 95%CI: 0.395-1.803, p=0.662;

Concerning the severe disease: Dyslipidemia OR 1.422; 95%CI: 0.81-2.50; p= 0.222; Hypertension OR 0.934; 95%CI: 0.556-0.1.57, p=0.78.

Diabetes was excluded from multivariate analyses; there was not significate association in bivariate analyses.

* It would be interesting to see how clustering of the metabolic risk factors (1, 2, 3, >3 MRF) impacted on the severity of COVID 19

We performed the statistical analyses for the MRF. Only the association of 3MRF was associated with the risk of hospitalization and only 4 MRF was associated with the risk of severe disease.

In multivariate analyse, the association of 3 MRF (obesity, diabetes, dyslipidemia) was associated with 2.3 fold risk of hospitalization (P=0.01) and not other
association. Only patients with >3 MRF seem to develop a severe disease OR 2.14
(95% 0.910-5.03, p=0.081)

* Authors should add a comment that this study only identify factors associated with the wild type of SARS COV2 (because is an early study in the first month of pandemics) and that the results probably cannot be generalized to the other variants

Indeed, the study was made in the first pandemic period (a 4 months period). Our message was that patients diagnosed with Covid who had NAFLD and NAFLD fibrosis and altered liver tests such as elevated transaminases had already pro inflammatory state might predispose to a severe disease. We agreed that it would be interesting to see how different variants may influence the severity of the disease, probably the inflammatory state in these high-risk patients may predispose to similar evolution and worse prognostic.
Abnormal liver tests and non-alcoholic fatty liver disease predict disease progression and
outcome of patients with Covid-19

Simona TRIPON¹, Pascal BILBAULT², Thibaut FABACHER³, Nicolas LEFEBVRE⁴, Sylvain
LESCUYER⁵, Emmanuel ANDRES⁶, Elise SCHMITT⁷, Sabrina GARNIER-KEPKA⁸, Pierrick Le
BORGNE⁹, Joris MULLER¹⁰, Hamid MERDJI¹¹, Frédéric CHAFFRAIX¹², Didier MUTTER¹³, Thomas
F BAUMERT¹⁴, Ferhat MEZIANI¹⁵, and Michel DOFFOEL¹⁶

¹Department of Hepatology and Gastroenterology, Service Expert de Lutte contre les
Hépatites Virales d'Alsace (SELHVA), Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux
Universitaires de Strasbourg, Institut des Maladies Virales et Hépatiques, Inserm U1110,
Strasbourg, France.

² Head of Emergency Department, Hôpitaux Universitaires de Strasbourg, Regenerative Nanomedicine, INSERM UMR 1260 Fédération de Médecine Translationelle de Strasbourg, University of Strasbourg, France

³Department of Public Health, Hôpitaux Universitaires de Strasbourg, France

⁴Department of Infectious Diseases, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

⁵Internal Medicine Department, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

⁶Department of Internal Medicine, Medical Clinic B, Hôpitaux Universitaires de Strasbourg, University of Strasbourg, France

⁷Geriatric Department, Hôpitaux Universitaires de Strasbourg, EA-3072, University of Strasbourg, France

⁸Emergency Department, Hôpitaux Universitaires de Strasbourg, France

⁹Emergency Department, Hôpitaux Universitaires de Strasbourg, Regenerative Nanomedicine, INSERM UMR 1260 Fédération de Médecine Translationelle de Strasbourg, University of Strasbourg, France

¹⁰Department of Public Health, Hôpitaux Universitaires de Strasbourg, France

¹¹Medical ICU, Médecine Intensive- Réanimation, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, INSERM UMR 1260, Regenerative Nanomedicine (RNM), FMTS, Université de Strasbourg, France.

¹²Service Expert de Lutte contre les Hépatites Virales d'Alsace (SELHVA), Pôle Hépato-digestif, Nouvel Hôpital Civil, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

¹³Department of General, Digestive, and Endocrine Surgery, Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France
¹⁴Department of Hepatology and Gastroenterology, Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Institut des Maladies Virales et Hépatiques, Inserm U1110, Institut Hopitalo-Universitaire, University of Strasbourg, France

¹⁵Medical ICU, Médecine Intensive - Réanimation, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, INSERM UMR 1260, Regenerative Nanomedicine (RNM), FMTS, Université de Strasbourg, France.

¹⁶Faculté de Médecine, University of Strasbourg, Inserm U1110, Strasbourg, France,

## Acknowledgments

We acknowledge all the members of SELHVA (Service Expert de Lutte contre les Hépatites Virales d'Alsace) for their contribution to this research. In addition, we acknowledge the different medical teams: Francois DANION, Yves HANSMANN, Charlotte KAEUFFER, Yvon RUCH (Department of Infectious Disease), Murielle RONDEAU-LUTZ, Juliette JEANNEL, Cornelia KUHNERT, Esther NOEL, Philippe MERTZ, Jean-Christophe WEBER (Department of Internal Medicine, Nouvel Hôpital Civil), Abrar-Ahmad ZULFIQAR, Hanane SALEM (Department of Internal Medicine, Medical clinic B, Nouvel Hôpital Civil), Samira FAFI-KREMER (Virology Laboratory), Bernard GOICHOT (Department of Internal Medicine, Diabetes and Metabolic Disorders, Hôpital de Hautepierre), Frédéric de BLAY (Department of Pneumology), Gérald ROUL (Departement of Cardiology), Thierry MARTIN (Department of Rheumatology), Thierry HANNEDOUCHE (Department of Nephrology), the physicians in charge of patients at the Emergency Room, and all the departments at the Hôpitaux Universitaires de Strasbourg that were in charge of patients with SARS-CoV-2 infection.
Moreover, we acknowledge all health-care workers involved in the diagnosis and treatment of COVID-19 patients, as well as the general management of the hospital for the organization of care in Hôpitaux Universitaires de Strasbourg. We thank all patients involved in the study.

## Authors' contributions:

ST conceived and designed the study, collected patient data, performed data analysis and interpretation, and drafted the manuscript. MD conceived and designed the study, analysis, and interpretation of the manuscript and contributed to the critical revision of the manuscript. FC, JM contributed to data collection. TFB, DM, FM contributed to the critical
revision of the manuscript. TF performed statistical analysis. All the others contributed to COVID-19 recruitment. All authors contributed to the manuscript revision and approved the submitted final version.

**Abbreviations:**

Alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), body mass index (BMI), coronavirus disease (COVID-19), fibrosis-4 (FIB-4), gamma-glutamyl transferase (GGT), hepatic steatosis index (HSI), liver function tests (LFTs), non-alcoholic fatty liver disease (NAFLD), NAFLD fibrosis score (NFS), capillary oxygen saturation (SaO2), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), total bilirubin (TBIL), prothrombin time (TP), upper limit of the normal (ULN).
**Abstract**

**Background and aims:** Coronavirus disease 2019 (COVID-19) is a serious public health issue that became rapidly pandemic. Liver injury and comorbidities, including metabolic syndrome, are associated with severe forms of the disease. This study sought to investigate liver injury, clinical features, and risk factors in patients with mild, moderate, and severe COVID-19.

**Methods:** We retrospectively included all consecutive patients hospitalized with laboratory-confirmed COVID-19 between February, 22 and May 15, 2020 at the emergency rooms of a French tertiary hospital. Medical history, symptoms, biological and imaging data were collected.

**Results:** Among the 1,381 hospitalizations for COVID-19, 719 patients underwent liver tests on admission and 496 (68.9%) patients displayed abnormal liver tests. Aspartate aminotransferase was most commonly abnormal in 57% of cases, followed by gamma-glutamyl transferase, alanine aminotransferase, albumin, alkaline phosphatase, and total bilirubin in 56.5%, 35.9%, 18.4%, 11.4%, and 5.8%. The presence of hepatocellular type more than 2xULN was associated with a higher risk of hospitalization and a worse course of severe disease (odd ratio [OR] 5.599; 95%CI: 1.27–23.86; p=0.021; OR 3.404; 95% CI: 2.12–5.47; p <0.001 respectively). A higher NAFLD fibrosis score was associated with a higher risk of hospitalization (OR 1.754; 95%CI: 1.27–2.43, p<0.001). In multivariate analyses, patients with high fibrosis-4 index had a 3-fold greater risk of severe disease (p <0.001).

**Conclusion:** Abnormal liver tests are common in patients with COVID-19 and could predict the outcome. Patients with non-alcoholic fatty liver disease and liver fibrosis are at higher risk of progressing to severe COVID-19.

**Key words:** SARS-CoV-2 infection, COVID-19 disease, liver function tests (LFTs), obesity, liver steatosis, liver fibrosis.
## Introduction

Coronaviruses are enveloped viruses that display the largest genome of all RNA viruses known to cause respiratory infections in humans (1). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the novel coronavirus disease that first occurred in 2019 (COVID-19) and became a serious global public health problem, declared by World Health Organization (WHO) as a global public health emergency. It can cause respiratory, intestinal, hepatic, and neuronal disease and may lead to acute respiratory distress syndrome, multiple organ failure, and death in severe cases (2).

As this infectious disease continues to spread, more information is needed to better identify the patients who require hospitalization or those who are likely develop severe COVID-19 disease. The identification of risk factors associated with outcomes should allow for patient stratification in view of personalized clinical care, resulting in outcome improvement.

Liver impairment has been commonly reported in COVID-19 patients (3). Abnormal liver function tests (LFTs) are present on admission, prior to initiating treatment with hepatotoxic drugs that may induce liver injury (4). However, the association between LFTs and different degrees of COVID-19 severity of is still poorly described.

The features of metabolic syndrome, including obesity, diabetes, and hypertension, are associated with a severe COVID-19 course (5). Non-alcoholic fatty liver disease (NAFLD) is associated with these metabolic comorbidities. The association and evolution of SARS-CoV-2 infection in patients with a preexisting liver disease like NAFLD are still incompletely understood.

The current study aimed to investigate liver injury, clinical features, and risk factors in patients with associated mild, moderate, and severe COVID-19.

## Patients and Methods

### Study design and participants

We retrospectively included all consecutive patients hospitalized for SARS-CoV-2 infection between February, 22 and May, 16, 2020 at the emergency rooms (ERs) of two French tertiary hospitals (Nouvel Hôpital Civil and Hôpital de Hautepierre - University Hospital of
Strasbourg). The study protocol was approved by the Ethics Committee of Strasbourg
University Hospital (CE-2020-138-26/08/2020).

Data Collection

The data collected for the hospitalized patients suffering from SARS-CoV-2 infection included their epidemiological and medical history, clinical signs, and biological and imaging data only at admission.

Three groups of patients were defined according to the hospitalization: (1) **mild disease**: patients hospitalized at the emergency room (ER) and discharged from the hospital without any indication of admission to a conventional hospitalization unit (CHU) or intensive care unit (ICU); (2) **moderate disease**: patients admitted to the ER and hospitalized thereafter in a CHU without signs of severe disease that would need intensive care support; (3) **severe disease**: patients needing intensive care support on admission or during the hospitalization in a CHU, patients hospitalized in the ICU, and patients who died during hospitalization in a CHU.

Liver tests were considered abnormal if the liver enzyme values, i.e., aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), and total bilirubin (TBIL), were elevated according to our laboratory upper limit of normal (ULN). Different patterns were defined: hepatocellular, cholestatic, and mixed patterns. The hepatocellular pattern was considered if AST or ALT were elevated higher than ULN; the cholestatic pattern if GGT or ALP were elevated up to ULN; the mixed pattern was a combination of the first two ones.

The exclusion criteria were described in the figure 1, as follows: patients with multiple hospitalizations for COVID-19, hospitalization for a pathology that may disturb LFTs (liver metastasis, cholangitis, cholecystitis, pancreatitis), patients transferred to other hospitals in Strasbourg or elsewhere, lost to follow-up or with missing clinical and biological data, patients with underlying chronic liver disease (viral, alcohol, autoimmune). Patients with diagnosed NAFLD were included in the study.
Moreover, NAFLD was evaluated using the hepatic steatosis index (HSI = 8 × [ALT/AST] + body mass index [BMI] [+ 2 if type 2 diabetes yes, + 2 if female]) >36 points (6) and the severity related to the stage fibrosis with NAFLD fibrosis score (NFS) (7). This HSI has been validated in COVID-19 patients to evaluate NAFLD (8-10). The fibrosis was also evaluated using fibrosis-4 index (FIB-4): low (<1.30), intermediate (1.30-2.67), and high (>2.67) (11). To evaluate the severity of the metabolic liver disease, we analyzed the different FIB-4 scores in patients diagnosed with NAFLD according to the HSI score.

Quantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) tests for SARS-CoV-2 nucleic acid were performed on admission with nasopharyngeal swabs. The primer and probe sequences targeted two regions on the RdRp gene and were specific to SARS-CoV-2. The assay sensitivity was around 10 copies/reaction (in-house method, Institut Pasteur, Paris, France) (12).

Chest computer tomography (CT) imaging was performed on admission for each patient. The images were classified based on the percentage of the whole lung parenchyma affected by COVID-19 in the following six groups: normal (no lesion), minimal (0%–10%), moderate (11%–25%), important (26%–50%), severe (51%–75%), and critical (>75%) (13). To simplify clinical data analysis, the patients were divided into three subgroups: ≤25%, 26%–50%, and >50%, according to their lesion extension.

## Statistics

The variables collected were expressed as the numbers and frequencies for qualitative variables (ordinal and categorical) and minimum, maximum, median, mean, and standard deviation for quantitative variables (discrete and continuous). These data were summarized in the descriptive phase.

For the bivariate analysis between groups, Fisher's exact test or Chi-squared tests were applied to compare qualitative variables, and Wilcoxon or Student t-tests were employed to compare quantitative variables.

To analyze the primary outcome, logistic regression was carried out by selecting variables from the literature, performing bivariate analysis, and then further selecting using Akaike information criterion (AIC).
Missing data were imputed by multiple imputations if they did not concern more than 30% of the data available; otherwise, the variable was excluded from the model.

The alpha risk was set at 5%, and all analyses were performed using R software, Version 4.0.

A multivariate analysis was performed after adjusting for confounding factors that might disturb the LFTs on admission, such as hypoxemia (capillary oxygen saturation [SaO₂] <93%, related to severe pneumopathy) (14) and hepatotoxic drugs (acetaminophen; antibiotics) administered prior to hospitalization. Hypotension, defined by systolic blood pressure <90mmHg, was not considered because of the low number of cases concerned (<10).

## Results

Between February, 22 and May, 15, 2020, a total of 1,381 patients with SARS-CoV-2 infection were admitted to the ERs. After excluding 662 patients according to our exclusion criteria, 719 were included in the subsequent analyses, as presented in the study flowchart (Figure 1). According to COVID-19 severity, 101(14%) patients displayed mild disease, 393 (54.7%) presented with moderate disease, and 225 (31.3%) developed severe disease.

Liver tests were performed on admission. The frequency of different abnormal LFTs, such as AST, ALT, ALP, GGT, TBIL, albumin, and prothrombin time (PT), was 57%, 35.9%, 11.4%, 56.5%, 5.8%, 18.4%, and 9.1%, respectively, while the mean values for these parameters were 53.8 (±85.1) u/L, 46.2 (±67.6) u/L, 74.2 (±39.1) u/L, 84.4 (±104.4) u/L, 11.3 (±14.5) µmol/L, 38.2 (±5.5) g/dL, and 87.8% (±14.7), respectively. The LFTs exhibited a hepatocellular pattern in 60.9%, cholestatic pattern in 63.1%, and mixed-type pattern in 46.8% of patients.

## Comparison between mild and moderate COVID-19 on admission

Table 1 summarizes the clinical and biological characteristics on admission for patients with mild and moderate disease.

LFT laboratory data showed higher elevations of AST (p<0.001), ALT (p=0.05), and GGT (p=0.004) in addition to lower albumin (p<0.001) and PT (p=0.042) in patients with moderate versus mild COVID-19 (Table 1). AST levels were higher in patients hospitalized in a CHU compared to those in ER: 1-2xULN (p=0.001) and >2xULN (p=0.001).

Patients with moderate COVID-19 exhibited elevated GGT levels compared to mild disease, 53.6% vs 16.7% (p=0.016) respectively and more disturbed hepatocellular (p=0.002), cholestatic (p=0.004) and mixed-type (p=0.004) patterns. These patients had also higher NFS
(p<0.001) and more severe liver fibrosis than patients with mild COVID-19: 71.7% had an
intermediate/high FIB-4 fibrosis score compared to 41.6% with mild disease (p<0.001).

Patients with moderate disease had more NAFLD with an intermediate/high FIB-4 score
compared to patients with mild disease (p<0.001) (Table 1).

As can be seen in Table 2, after adjusting for confounding factors that could disturb LFTs,
such as hypoxemia and hepatotoxic drugs, patients with AST 2xULN and hepatocellular
pattern 2xULN had a 5.5-fold higher risk of being hospitalized (OR 5.599; 95% CI: 1.29–24.25,
p=0.021 and OR 5.499; 95%CI: 1.27–23.86; p=0.023, respectively). Patients with metabolic
syndrome (the association of 3 metabolic risk factors (MRF)) had more than 2.5-fold higher
risk of hospitalization (OR 2.56; 95%CI: 1.292–5.09, p=0.007).

Moderate disease was associated with an intermediate (OR 2.405; 95%CI: 1.39–4.15;
p=0.002) and high (OR 5.415; 95%CI: 2.69–10.89; p< 0.001) FIB-4 score. Similarly, patients
with an elevated NFS had a higher risk of hospitalization (OR 1.754 95%CI: 1.27–2.43;
p<0.001).

In multivariate analyses, patients with metabolic syndrome had almost a 2.4-fold risk of
being hospitalized (OR 2.37; 95% CI: 1.18–4.75, p=0.015).

Comparison between mild/moderate and severe COVID-19

We compared the clinical and laboratory tests and radiological findings in patients with
severe versus those with moderate and mild COVID-19 disease (Table 3).

The laboratory liver tests showed more elevated AST (p<0.001), ALT (p=0.002), and TBIL
levels (p=0.01), in addition to lower albumin (p<0.001) and PT (p<0.001) in severe disease
patients (Table 3).

Concerning abnormal LFT patterns, severe COVID-19 was associated with all types of
patterns: hepatocellular (p<0.001), cholestatic (p=0.028), and mixed (p<0.001).

In severe disease patients, 89.3% had intermediate and high FIB-4 scores compared to 65.5%
of patients with moderate and mild disease (p<0.001). More patients with severe COVID-19
had NAFLD associated with a high FIB-4: 72.8% vs. 57.9%, respectively (p<0.001) (Table 3).
After adjusting for confounding factors, as described, abnormal LFTs at admission such as AST>2x ULN (OR 3.187; 1.99–5.08; p< 0.001), hepatocellular pattern >2x ULN (2.265; 95% CI 1.48–3.45; p< 0.001), intermediate and high FIB-4 score (OR 2.006; 95%CI 1.09–3.67; p=0.024 and OR 4.756; 95%CI: 2.68–8.43; p<0.001 respectively) or metabolic syndrome (OR 2.45, 95%CI1.07-5.61, p=0.03) were associated with a worse evolution of the disease (Table 4).

In multivariate analyses, patients with high FIB-4 index had 3-fold risk to develop a severe disease (OR 3.11; 95%CI 1.4-6.91, p=0.005).

Discussion

Coronavirus disease 2019, the pandemic caused by SARS-CoV-2, has been associated with substantial morbidity and mortality (15). More information concerning SARS-CoV-2 infection is needed to rapidly identify patients likely to develop severe disease, requiring adjusted care. This is the first monocentric study that describes the outcome of a large cohort of COVID-19 patients based on clinical, laboratory, and radiological data collected at admission, enabling us to rapidly identify factors associated with outcomes that predict mild, moderate, or severe disease at the time of hospital admission the hospital.

Recently published studies have shown that patients admitted for COVID-19 often present with abnormal liver test, the incidence of liver injury ranging from 14% to 78% (1, 6, 16). Moreover, preexisting liver disease has been shown to be associated with worsening infection prognosis (17). However, the mechanistic events remain to be determined, and the causes of LFTs may be multifactorial, including causes unrelated to COVID-19. In contrast to previous studies, we analyzed only the laboratory liver tests at baseline in order to avoid the anomalies induced by different drugs administrated during the hospitalization.

In this present cohort, on admission, abnormal LFTs were present in 68.9% of patients, being more elevated than those previously reported in China at 14.9%, yet similar to those estimated in the United States at between 40%–67.5%. This controversy could be explained by the higher proportion of obese and diabetic patients in occidental countries (18).
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Hepatocellular parameters, such as the AST and ALT, were elevated in 57% and 35.9% of patients, respectively, being beyond 2xULN in 17.4% and 4%, respectively. The patients with severe disease had a higher AST compared with moderate and mild disease patients (p<0.001). The ALT was also more elevated in severe COVID-19 patients when compared with the other patient groups (p=0.02). After adjusting for confounding factors, elevated transaminases, such as AST >2xULN, and hepatocellular pattern type >2xULN were associated with a 3-fold (p<0.001) and almost 3.5-fold (p<0.001) higher risk, respectively, of a poorer clinical outcome.

Recent studies have shown that SARS-CoV-2 binds angiotensin converting enzyme 2 (ACE2) receptors localized both in the liver and bile duct cells. The expression is higher in the cholangiocytes that may be associated with cholestatic abnormalities (elevated ALP and GGT) rather than hepatocellular abnormalities (AST and ALT) (1, 20).

In our study, the ALP, GGT, and TBIL were elevated in 11.4%, 56.5%, and 5.8% of patients, respectively. Only an abnormal GGT was associated with moderate disease (GGT ULN, OR 0.108; 95%CI: 0.013–0.89, p=0.039). Interestingly, ALP >1–2xULN was more common in moderate and mild disease (91.5%) than in severe disease (83.9%) (p=0.045). Our data confirm the hypothesis of Hundt et al. reporting that the mechanism of liver damage may be not caused by direct cytopathic effects of the novel coronavirus (19).

In recent studies, obesity was associated with COVID-19 disease severity (21, 22). Indeed, in our study, 72.5% of patients that required hospitalization were overweight, with 34.6% being obese. The proportion of overweight patients increased with disease severity, as follows: 58.4% in mild, 71.8% in moderate, and 73.3% in severe COVID-19. Moreover, the hospitalized patients had a higher BMI compared to those discharged from an ER: 28.9 vs. 26.9Kg/m² (p=0.001).

A strong association between obesity and the severity of COVID-19 was previously reported, even in the absence of other comorbidities, yet the underlying mechanism is unclear (23).

Obesity is characterized by a chronic inflammatory state and disrupted immune response. The main cytokines responsible for chronic inflammation are tumor necrosis factor-alpha (TNF-α), interleukin (Il)-6, and IL-1 beta(β) (5). Moreover, SARS-CoV-2 may cause a hyper-inflammatory reaction through the excessive release of cytokines, a condition known as
"cytokine storm," that may be enhanced by obesity, particularly in patients with a preexisting liver disease like NAFLD (24). Adipose tissue may also be a viral reservoir and an immunological hub for the inflammatory response (25). Individuals with severe obesity, diabetes, and hypertension are more likely to be infected and are at a higher risk of complications and death due to COVID-19 (2).

Indeed, in our study, metabolic factors were associated with the severity of COVID-19. Indeed, hypertension (p=0.002) and dyslipidemia (p=0.001) were associated with moderate disease, and diabetes was associated with a severe course of the disease (p=0.032). Patients with metabolic syndrome had a 2.5-fold higher risk to develop a moderate (p=0.007) or a severe disease (p=0.033).

Abnormal GGT is commonly observed in NAFLD and is a sensitive marker of steatosis (11). In our study, the ALP was elevated only in 11.4% of patients, and the mean values were not significantly higher in mild disease compared to moderate (p=0.938) and severe forms (p=0.178). Moreover, 56.5% of patients had abnormal GGT levels, and this was correlated with the severity of COVID-19 when comparing severe versus moderate (p=0.02) and mild disease forms (p=0.016).

The prognosis of liver mortality was related to liver fibrosis (26). Liver fibrosis evaluated with the FIB-4 score was associated with the severity of COVID-19 illness: 71.7% of patients with intermediate/high FIB-4 scores needed hospitalization compared to 41.6% of those with only mild disease (p<0.001), and it was higher in patients with severe SARS-COV-2 infection than in those with moderate and mild disease (89.3% compared to 65.5%, respectively) (p<0.001). Patients who needed hospitalization had a more elevated NFS compared to those who were discharged from an ER (p=0.001).

We associated the FIB-4 scores in patients diagnosed with NAFLD (Tables 2 and 4). In the hospitalized patients, 84.2% had an intermediate/high FIB-4 compared to 67.3% of those discharged from an ER (p<0.001). In patients with a high FIB-4, 72.8% had severe disease compared to the 57.9% of those with moderate and mild disease (p<0.001). Moreover, the patients with NAFLD without fibrosis (FIB-4<1.3) were at a lower risk of hospitalization (OR 0.404; 95%CI: 0.20-0.81; p<0.011).
In the multivariate-adjusted analysis, patients with high FIB-4 scores had an almost 3-fold greater risk of hospitalization (p=0.011), and a worse course of disease (p<0.001).

In the context of the COVID-19 pandemic, along with the high prevalence of NAFLD in the general population, it is mandatory to early identify patients at risk of developing a severe disease. Applying non-invasive tests such as the FIB-4 and NFS in patients with metabolic liver disease could enable us to better and more quickly identify these patients that may have a poorer prognosis (27).

Our study had some limitations. First, the data were collected retrospectively, and there was missing information regarding the tobacco and alcohol consumption, several clinical parameters (waist and hip circumferences), and laboratory tests pertaining to metabolic syndrome, including glucose, glycated hemoglobin, cholesterol, and triglycerides levels. Second, while all patients underwent transaminase measurements at admission, only half of them underwent cholestasis enzyme assessment at admission, which could be deemed as a limitation in the statistical analysis. Third, HIS index was used in several publications to define NAFLD in COVID 19 patients (8-10). It is a simple, disponible score that may help us to easy identify high-risk patients. His limitation is related to the elevated transaminases levels that could underestimate the index.

In conclusion, obese patients with metabolic syndrome and a preexisting liver disease, including NAFLD and liver fibrosis, display a dysregulated hepatic innate immunity and pro-inflammatory state that is likely to enhance the “cytokine storm” due to SARS CoV-2 infection, leading to severe COVID-19 disease. Thus, abnormal LFTs at admission predict the outcome of COVID-19 patients.

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<caption>Figure 1. Flowchart of patients included in the study</caption>
<figure><img src="image_3.png" /><figcaption>Table 1. Characteristics of patients with mild and moderate COVID-19 on admission</figcaption></figure>
<table><thead><tr><th>Variable</th><th>Mild COVID-19 (n=101)</th><th>Moderate COVID-19 (n=393)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years, SD*)</td><td>57.7 (18.4)</td><td>65.7 (7)</td><td>&lt;0.001</td></tr><tr><td>Gender (male, %)</td><td>52 (51.4)</td><td>198 (50.4)</td><td>0.468</td></tr><tr><td>BMI (kg/m², SD)</td><td>26.9 (4.9)</td><td>28.8 (5)</td><td>0.004</td></tr><tr><td><strong>Comorbidity</strong></td><td></td><td></td><td></td></tr><tr><td>Diabetes (n, %)</td><td>21 (20.8)</td><td>105 (26.7)</td><td>0.251</td></tr><tr><td>Hypertension (n, %)</td><td>38 (37.6)</td><td>217 (55.2)</td><td>0.002</td></tr><tr><td>Dyslipidemia (n, %)</td><td>17 (16.8)</td><td>129 (32.8)</td><td>0.001</td></tr><tr><td>Chronic pulmonary disease (n, %)</td><td>19 (18.8)</td><td>94 (24)</td><td>0.291</td></tr><tr><td>Chronic cardiac disease (n, %)</td><td>19 (18.8)</td><td>106 (27)</td><td>0.097</td></tr><tr><td>Chronic kidney disease (n, %)</td><td>4 (4)</td><td>26 (6.6)</td><td>0.482</td></tr><tr><td>Cancer (n, %)</td><td>2 (2)</td><td>20 (5.1)</td><td>0.277</td></tr><tr><td><strong>Symptoms onset</strong></td><td></td><td></td><td></td></tr><tr><td>"Flu-like" symptoms (n, %)</td><td>87 (86.1)</td><td>366 (93.1)</td><td>0.041</td></tr><tr><td>Respiratory symptoms (n, %)</td><td>87 (86.1)</td><td>351 (89.3)</td><td>0.014</td></tr><tr><td>Digestive symptoms (n, %)</td><td>40 (39.6)</td><td>208 (52.9)</td><td>0.019</td></tr><tr><td>Neurological symptoms (n, %)</td><td>45 (44.6)</td><td>184 (46.8)</td><td>0.738</td></tr><tr><td><strong>Clinical features</strong></td><td></td><td></td><td></td></tr><tr><td>Oxygen saturation SaO2 (%, SD)</td><td>96 (3.9)</td><td>92.1 (5.9)</td><td>&lt;0.001</td></tr><tr><td>Debit of oxygen at admission (L/mn, sd)</td><td>0.4 (1.8)</td><td>2.3 (7.4)</td><td>&lt;0.001</td></tr><tr><td>Systolic arterial pressure (mmHg, SD)</td><td>132.4 (17.9)</td><td>133.1 (25.8)</td><td>0.683</td></tr><tr><td><strong>Laboratory tests</strong></td><td></td><td></td><td></td></tr><tr><td>AST U/L (reference range 11-34)</td><td>30.4 (20.4)</td><td>45.5 (36.2)</td><td>&lt;0.001</td></tr><tr><td>ALT U/L (reference range 8-41)</td><td>35.7 (27.5)</td><td>42.3 (34)</td><td>0.05</td></tr><tr><td>ALP U/L (reference range 41-117)</td><td>66.1 (13.2)</td><td>76.1 (40.6)</td><td>0.938</td></tr><tr><td>GGT U/L (reference range 6-40)</td><td>30.2 (23.7)</td><td>87.6 (119.2)</td><td>0.004</td></tr><tr><td>Bilirubin µmol/L, (reference range 1.7-21.0)</td><td>9.1 (4.9)</td><td>11.3 (18.3)</td><td>0.279</td></tr><tr><td>Albumin g/L, (reference range 35-50)</td><td>43.1 (3.9)</td><td>39.2 (5)</td><td>&lt;0.001</td></tr><tr><td>Abnormality type (n, %)</td><td></td><td></td><td></td></tr><tr><td>Hepatocellular &gt; ULN</td><td>39 (38.6)</td><td>221 (56.2)</td><td>0.002</td></tr><tr><td>Cholestatic &gt; ULN</td><td>2 (16.7)</td><td>112 (61.2)</td><td>0.004</td></tr><tr><td>Mixed &gt; ULN</td><td>0 (0)</td><td>72 (39.3)</td><td>0.004</td></tr><tr><td>PT (normal range &gt;70%) (%, SD)</td><td>92.6 (8)</td><td>87.9 (15.7)</td><td>0.042</td></tr><tr><td>CRP mg/L (reference range &lt; 4)</td><td>36.1 (54.1)</td><td>87.9 (70.5)</td><td>&lt;0.001</td></tr><tr><td>Leucocytes *10<sup>9</sup>/L (reference range 4.5-6.5)</td><td>6.2 (2.6)</td><td>7(3.4)</td><td>0.02</td></tr><tr><td>Neutrophil count *10<sup>9</sup>/L (reference range 1.3-7.7)</td><td>4.2 (2.2)</td><td>5.3 (3)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L (reference range 1.0-4.0)</td><td>1.5 (0.8)</td><td>1.2 (1.3)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L ≤1.0 (n, %)</td><td>31 (30.7)</td><td>200 (50.9)</td><td>0.001</td></tr><tr><td>Fibrinogen g/L (reference range: 2-4)</td><td>5(1.4)</td><td>5.9 (1.5)</td><td>0.016</td></tr></tbody></table>
<table><tr><td>Platelets *10<sup>9</sup>/L<br>(reference range: 150-400)</td><td>236.2 (104.6)</td><td>226.3 (99.8)</td><td>0.331</td></tr><tr><td>D-Dimers (reference range: 150-400)</td><td>765 (698)</td><td>2452.9 (4111.5)</td><td>0.05</td></tr><tr><td><strong>Metabolic and fibrosis characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>FIB-4 status (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>&lt;1.3</td><td>59 (58.4)</td><td>111 (28.3)</td><td></td></tr><tr><td>1.3-2.67</td><td>29 (28.7)</td><td>142 (36.2)</td><td></td></tr><tr><td>&gt;2.67</td><td>13 (12.9)</td><td>139 (35.5)</td><td></td></tr><tr><td>NAFLD (n, %)</td><td>62 (61.4)</td><td>249 (63.4)</td><td>0.73</td></tr><tr><td>NAFLD+ et FIB-4 (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>NAFLD + FIB-4 &lt;1.3</td><td>33 (32.7)</td><td>62 (15.8)</td><td></td></tr><tr><td>NAFLD + FIB-4 1.3-2.67</td><td>19 (18.8)</td><td>94 (23.9)</td><td></td></tr><tr><td>NAFLD + FIB-4 &gt;2.67</td><td>49 (48.5)</td><td>237 (60.3)</td><td></td></tr><tr><td>NFS (mean, SD)</td><td>-2 (1.5)</td><td>-0.7 (1.7)</td><td>&lt;0.001</td></tr><tr><td><strong>Radiologic findings</strong></td><td></td><td></td><td></td></tr><tr><td>Severity of pneumonia (CT scan)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>-absence</td><td>25 (26%)</td><td>24 (6.8%)</td><td></td></tr><tr><td>-&lt;25%</td><td>58 (60.4%)</td><td>185 (52.6%)</td><td></td></tr><tr><td>25-50%</td><td>10 (10.4%)</td><td>116 (33%)</td><td></td></tr><tr><td>&gt;50%</td><td>3 (3.1%)</td><td>27 (7.7%)</td><td></td></tr><tr><td>Delays from symptom onset to<br>hospitalization (days, SD)</td><td>7.2 (6.8)</td><td>7.6 (6.8)</td><td>0.592</td></tr><tr><td>Time of hospitalization (days, SD)</td><td>1 (0.9)</td><td>10.6 (8.5)</td><td>&lt;0.001</td></tr></table>

BMI: body mass index

SD: standard deviation

COVID-19: coronavirus disease 2019

AST: aspartate aminotransferase

ALT: alanine aminotransferase

ALP: alkaline phosphatase

ULN: upper limit of the normal

GGT: gamma-glutamyl transferase

LFTs: liver function tests

PT: prothrombin time

FIB-4: fibrosis-4

CRP: Creatine-reactive protein

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2

CT: computed tomography

HSI: hepatic steatosis index

NAFLD: non-alcoholic fatty liver disease

NFS: NAFLD fibrosis score

SaO2: capillary oxygen saturation

TBIL: total bilirubin
<table><caption>Table 2. Factors associated with moderate COVID-19, after adjusting for confounding factors</caption><thead><tr><td></td><td>OR</td><td>95%CI</td><td>p-value</td></tr></thead><tbody><tr><td>MS (MRF 1+2+3)</td><td>2.85</td><td>1.49-5.45</td><td>0.002</td></tr><tr><td>MS (MRF 1+2+4)</td><td>2.56</td><td>1.29-5.09</td><td>0.007</td></tr><tr><td>MS (MFR 1+3+4)</td><td>3.09</td><td>1.29-6.89</td><td>0.006</td></tr><tr><td>NFS score</td><td>1.754</td><td>1.27-2.43</td><td>&lt;0.001</td></tr><tr><td>FIB-4 (1.3-2.67)</td><td>2.405</td><td>1.39-4.15</td><td>0.002</td></tr><tr><td>FIB-4 &gt;2.67</td><td>5.415</td><td>2.69-10.89</td><td>&lt;0.001</td></tr><tr><td>NAFLD+ and FIB-4&lt;1.30</td><td>0.404</td><td>0.20-0.81</td><td>0.011</td></tr><tr><td>ASTULN</td><td>0.395</td><td>0.23-0.66</td><td>&lt;0.001</td></tr><tr><td>AST&gt;2 ULN</td><td>5.599</td><td>1.29-24.25</td><td>0.021</td></tr><tr><td>Hepatocellular type &gt;2 ULN</td><td>5.499</td><td>1.27-23.86</td><td>0.023</td></tr><tr><td>GGT ULN</td><td>0.108</td><td>0.01-0.89</td><td>0.039</td></tr><tr><td>Cholestatic type &gt;ULN</td><td>11.915</td><td>1.45-98.27</td><td>0.021</td></tr><tr><td>Albumin</td><td>0.805</td><td>0.70-0.92</td><td>0.002</td></tr><tr><td>Neutrophil count</td><td>1.13</td><td>1.02-1.25</td><td>0.023</td></tr><tr><td>Lymphocytes&lt;1000/µL</td><td>2.196</td><td>1.33-3.64</td><td>0.002</td></tr><tr><td>Prothrombin time</td><td>0.963</td><td>0.94-0.99</td><td>0.01</td></tr><tr><td>CRP</td><td>1.017</td><td>1.01-1.02</td><td>&lt;0.001</td></tr><tr><td>CRP &lt;100 mg/L</td><td>0.29</td><td>0.14-0.62</td><td>&lt;0.001</td></tr></tbody></table>

OR: odd ratio; CI: confidence interval; MS: metabolic syndrome, MRF: metabolic risk factors: 1-obesity; 2-diabetes, 3-dyslipidemia, 4-hypertension.

Adjustment for confounding factors that could disturb LFTs, as follows: hypoxemia (SaO2 < 93% related to severe pneumopathy) (16); hepatotoxic drugs (acetaminophen, antibiotics) administrated before hospitalization. Hypotension defined by systolic blood pressure < 90mmHg was not considered because of low case number <10
<table><caption>Table 3. Characteristics of patients with mild and moderate versus severe COVID-19 on admission.</caption><thead><tr><th>Variable</th><th>Mild and Moderate COVID-19 (n=494)</th><th>Severe COVID-19 (n=225)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years, SD*)</td><td>63.1 (17.5)</td><td>68.3 (16.9)</td><td>&lt;0.001</td></tr><tr><td>Gender (male, %)</td><td>251 (50.8)</td><td>138 (61.3)</td><td>0.94</td></tr><tr><td>BMI (Kg/m²) (mean, SD)</td><td>28.4 (5.9)</td><td>28.9 (5.9)</td><td>0.255</td></tr><tr><td><strong>Comorbidity</strong></td><td></td><td></td><td></td></tr><tr><td>Diabetes (n, %)</td><td>126 (25.5)</td><td>75 (33.3)</td><td>0.032</td></tr><tr><td>Hypertension (n, %)</td><td>255 (51.6)</td><td>131 (58.2)</td><td>0.107</td></tr><tr><td>Dyslipidemia (n, %)</td><td>146 (29.6)</td><td>80 (35.7)</td><td>0.118</td></tr><tr><td>Chronic pulmonary disease (n, %)</td><td>113 (23)</td><td>63 (28)</td><td>0.161</td></tr><tr><td>Chronic cardiac disease (n, %)</td><td>125 (25.3)</td><td>72 (32)</td><td>0.071</td></tr><tr><td>Chronic kidney disease (n, %)</td><td>30 (6.1)</td><td>29 (12.9)</td><td>0.003</td></tr><tr><td>Cancer (n, %)</td><td>22 (4.5)</td><td>10 (4.5)</td><td>1</td></tr><tr><td><strong>Symptoms onset</strong></td><td></td><td></td><td></td></tr><tr><td>“Flu-like” symptoms (n, %)</td><td>453 (91.7)</td><td>210 (93.3)</td><td>0.549</td></tr><tr><td>Respiratory symptoms (n, %)</td><td>438 (88.7)</td><td>208 (92.4)</td><td>0.435</td></tr><tr><td>Digestive symptoms (n, %)</td><td>248 (50.2)</td><td>96 (42.7)</td><td>0.064</td></tr><tr><td>Neurological symptoms (n, %)</td><td>229 (46.4)</td><td>106 (47.1)</td><td>0.872</td></tr><tr><td><strong>Clinical features</strong></td><td></td><td></td><td></td></tr><tr><td>Oxygen saturation SaO2 (%, SD)</td><td>92.9 (5.7)</td><td>85.3 (9.1)</td><td>&lt;0.001</td></tr><tr><td>Debit of oxygen at admission (l/mn, sd)</td><td>1.9 (6.7)</td><td>6.5 (7.7)</td><td>&lt;0.001</td></tr><tr><td>Systolic arterial pressure, mmHg</td><td>132.5 (20.8)</td><td>131.5 (20.9)</td><td>0.375</td></tr><tr><td><strong>Laboratory tests</strong></td><td></td><td></td><td></td></tr><tr><td>AST U/L (reference range, 11-34)</td><td>48 (87.8)</td><td>73.1 (64.6)</td><td>&lt;0.001</td></tr><tr><td>ALT U/L (reference range, 8-41)</td><td>40.9 (32.8)</td><td>57.9 (110)</td><td>0.002</td></tr><tr><td>ALP U/L (reference range, 41-117)</td><td>75.2 (39)</td><td>72.3 (39.4)</td><td>0.178</td></tr><tr><td>GGT U/L (reference range, 6-40)</td><td>84.2 (116.5)</td><td>84.7 (79.6)</td><td>0.087</td></tr><tr><td>Total Bilirubin µmol/l (reference range, 1.7-21.0)</td><td>11.1 (17.5)</td><td>11.6 (6.9)</td><td>0.01</td></tr><tr><td>Conjugated Bilirubin µmol/l (reference range &lt; 4.3)</td><td>3.7 (2.6)</td><td>4.2 (2.4)</td><td>0.055</td></tr><tr><td>Albumin g/, (reference range, 35-50)</td><td>39.7 (5)</td><td>36.4 (5.4)</td><td>&lt;0.001</td></tr><tr><td>PT (normal range &gt;70%) (%, SD)</td><td>88.9 (14.6)</td><td>85.6 (14.7)</td><td>&lt;0.001</td></tr><tr><td><strong>Abnormality type (n, %)</strong></td><td></td><td></td><td></td></tr><tr><td>Hepatocellular &gt;ULN</td><td>260 (52.6)</td><td>178 (79.1)</td><td>&lt;0.001</td></tr><tr><td>Cholestatic &gt;ULN</td><td>114 (58.5)</td><td>81 (71.1)</td><td>0.028</td></tr><tr><td>Mixed type &gt;ULN</td><td>72 (36.9)</td><td>72 (63.7)</td><td>&lt;0.001</td></tr><tr><td>CRP mg/L (reference range &lt; 4)</td><td>77.7 (70.6)</td><td>148.7 (92.6)</td><td>&lt;0.001</td></tr><tr><td>Leucocytes *10<sup>9</sup>/l</td><td>6.9 (3.3)</td><td>8.6 (4.4)</td><td>&lt;0.001</td></tr></tbody></table>
<table><tbody><tr><td>(reference range 4.5-6.5)</td><td></td><td></td><td></td></tr><tr><td>Neutrophil count *10<sup>9</sup>/l<br>(reference range 1.3-7.7)</td><td>5.1 (2.9)</td><td>6.9 (3.9)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/l<br>(reference range: 1.0-4.0)</td><td>1.2 (1.2)</td><td>1 (1.5)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L<br>≤ 1.0 (n, %)</td><td>231 (46.8)</td><td>137 (60.9)</td><td>0.001</td></tr><tr><td>Fibrinogen g/l (reference range 2-4)</td><td>5.8 (1.5)</td><td>6.8 (1.4)</td><td>&lt;0.001</td></tr><tr><td>Platelets *10<sup>9</sup>/L<br>(reference range 150-400)</td><td>228.3 (99.8)</td><td>210.6 (5.4)</td><td>0.028</td></tr><tr><td>D-Dimers (reference range 150-400)</td><td>2302.9 (3956.9)</td><td>2849.6 (4072.5)</td><td>0.001</td></tr><tr><td><strong>Metabolic and fibrosis characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>FIB-4 score (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>≤1.3</td><td>170 (34.5)</td><td>24 (10.7)</td><td></td></tr><tr><td>1.3-2.67</td><td>171 (34.7)</td><td>67 (29.9)</td><td></td></tr><tr><td>&gt;2.67</td><td>152 (30.8)</td><td>133 (59.4)</td><td></td></tr><tr><td>NAFLD (n, %)</td><td>311 (63)</td><td>134 (59.8)</td><td>0.455</td></tr><tr><td>NAFLD+ et FIB-4 (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>NAFLD + FIB-4 &lt;1.3</td><td>95 (19.2)</td><td>20 (8.9)</td><td></td></tr><tr><td>NAFLD + FIB-4 1.3-2.67</td><td>113 (22.9)</td><td>41 (18.3)</td><td></td></tr><tr><td>NAFLD + FIB-4 &gt;2.67</td><td>286 (57.9)</td><td>163 (72.8)</td><td></td></tr><tr><td>NAFLD fibrosis score (mean, SD)</td><td>-0.8 (1.8)</td><td>-0.6 (1.6)</td><td>0.435</td></tr><tr><td><strong>Radiologic findings</strong></td><td></td><td></td><td></td></tr><tr><td>Severity of pneumonia (CT scan)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>-absence (n, %)</td><td>49 (10.9%)</td><td>10 (5%)</td><td></td></tr><tr><td>-&lt;25%</td><td>243 (54.2%)</td><td>53 (26.6%)</td><td></td></tr><tr><td>25-50%</td><td>126 (28.1%)</td><td>75 (37.7%)</td><td></td></tr><tr><td>&gt;50%</td><td>30 (6.7%)</td><td>61 (30.7%)</td><td></td></tr><tr><td>Delays from symptoms onset to<br>hospitalization (days, SD)</td><td>6.6 (6.2)</td><td>7.2 (5.3)</td><td>0.062</td></tr><tr><td>Time of hospitalization</td><td>8.7 (8.5)</td><td>19.1 (12.8)</td><td>&lt;0.001</td></tr></tbody></table>

SD, standard deviation

mean values

BMI: body mass index

SD: standard deviation

COVID-19: coronavirus disease 2019

AST: aspartate aminotransferase

ALT: alanine aminotransferase

ALP: alkaline phosphatase

ULN: upper limit of the normal

GGT: gamma-glutamyl transferase

LFTs: liver function tests

PT: prothrombin time
FIB-4: fibrosis-4

CRP: Creatine-reactive protein

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2

CT: computed tomography

HSI: hepatic steatosis index

NAFLD: non-alcoholic fatty liver disease

NFS: NAFLD fibrosis score

SaO2: capillary oxygen saturation

TBIL: total bilirubin

<table><caption>Table 4. Factors associated with moderate severe COVID-19 after adjusting for confounding factors.</caption><thead><tr><td></td><td>OR</td><td>95%CI</td><td>p-value</td></tr></thead><tbody><tr><td>MS (MRF 1+2+3+4)</td><td>2.45</td><td>1.07-5.61</td><td>0.033</td></tr><tr><td>NFS</td><td>1.181</td><td>1.01-1.37</td><td>0.033</td></tr><tr><td>FIB-4 (1.3-2.67)</td><td>2.006</td><td>1.09-3.67</td><td>0.024</td></tr><tr><td>FIB-4 > 2.67</td><td>4.756</td><td>2.68-8.43</td><td>&lt;0.001</td></tr><tr><td>AST ULN</td><td>0.406</td><td>0.27-0.61</td><td>&lt;0.001</td></tr><tr><td>AST >2 ULN</td><td>3.187</td><td>1.99-5.08</td><td>&lt;0.001</td></tr><tr><td>ALT ULN</td><td>0.716</td><td>0.48-1.05</td><td>0.09</td></tr><tr><td>Hepatocellular type>ULN</td><td>2.265</td><td>1.48-3.45</td><td>&lt;0.001</td></tr><tr><td>Hepatocellular type >2 ULN</td><td>3.404</td><td>2.12-5.47</td><td>&lt;0.001</td></tr><tr><td>GGT>1-3 ULN</td><td>1.676</td><td>0.97-2.88</td><td>0.062</td></tr><tr><td>Albumin ULN</td><td>0.422</td><td>0.21-0.82</td><td>0.01</td></tr><tr><td>Albumin</td><td>0.871</td><td>0.82-0.92</td><td>&lt;0.001</td></tr><tr><td>Leucocytes count</td><td>1.104</td><td>1.05-1.16</td><td>&lt;0.001</td></tr><tr><td>Neutrophil count</td><td>1.136</td><td>1.07-1.21</td><td>&lt;0.001</td></tr><tr><td>CRP >200 mg/L</td><td>2.785</td><td>1.66-4.66</td><td>&lt;0.001</td></tr><tr><td>CRP <100 mg/L</td><td>0.441</td><td>0.29-0.65</td><td>&lt;0.001</td></tr><tr><td>Fibrinogen</td><td>1.317</td><td>1.05-1.64</td><td>0.015</td></tr><tr><td>Lupus-anticoagulant</td><td>4.453</td><td>1.49-13.23</td><td>0.007</td></tr></tbody></table>

OR: odd ratio; CI: confidence interval; MS: metabolic syndrome, MRF: metabolic risk factors: 1-obesity; 2-diabetes, 3-dyslipidemia, 4-hypertension.

Adjustment for confounding factors that could disturb LFTs, as follows: hypoxemia (SaO2 < 93% related to severe pneumopathy) (16), and hepatotoxic drugs (acetaminophen, antibiotics) administrated before the hospitalization. Hypotension defined by systolic blood pressure <90mmHg was not considered because of the low number of cases <10.
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Abnormal liver tests and non-alcoholic fatty liver disease predict disease progression and
outcome of patients with Covid-19

Simona TRIPON¹, Pascal BILBAULT², Thibaut FABACHER³, Nicolas LEFEBVRE⁴, Sylvain
LESCUYER⁵, Emmanuel ANDRES⁶, Elise SCHMITT⁷, Sabrina GARNIER-KEPKA⁸, Pierrick Le
BORGNE⁹, Joris MULLER¹⁰, Hamid MERDJI¹¹, Frédéric CHAFFRAIX¹², Didier MUTTER¹³, Thomas
F BAUMERT¹⁴, Ferhat MEZIANI¹⁵, and Michel DOFFOEL¹⁶

¹Department of Hepatology and Gastroenterology, Service Expert de Lutte contre les
Hépatites Virales d'Alsace (SELHVA), Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux
Universitaires de Strasbourg, Institut des Maladies Virales et Hépatiques, Inserm U1110,
Strasbourg, France.

² Head of Emergency Department, Hôpitaux Universitaires de Strasbourg, Regenerative Nanomedicine, INSERM UMR 1260 Fédération de Médecine Translationelle de Strasbourg, University of Strasbourg, France

³Department of Public Health, Hôpitaux Universitaires de Strasbourg, France

⁴Department of Infectious Diseases, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

⁵Internal Medicine Department, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

⁶Department of Internal Medicine, Medical Clinic B, Hôpitaux Universitaires de Strasbourg, University of Strasbourg, France

⁷Geriatric Department, Hôpitaux Universitaires de Strasbourg, EA-3072, University of Strasbourg, France

⁸Emergency Department, Hôpitaux Universitaires de Strasbourg, France

⁹Emergency Department, Hôpitaux Universitaires de Strasbourg, Regenerative Nanomedicine, INSERM UMR 1260 Fédération de Médecine Translationelle de Strasbourg, University of Strasbourg, France

¹⁰Department of Public Health, Hôpitaux Universitaires de Strasbourg, France

¹¹Medical ICU, Médecine Intensive- Réanimation, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, INSERM UMR 1260, Regenerative Nanomedicine (RNM), FMTS, Université de Strasbourg, France.

¹²Service Expert de Lutte contre les Hépatites Virales d'Alsace (SELHVA), Pôle Hépato-digestif, Nouvel Hôpital Civil, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

¹³Department of General, Digestive, and Endocrine Surgery, Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France
¹⁴Department of Hepatology and Gastroenterology, Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Institut des Maladies Virales et Hépatiques, Inserm U1110, Institut Hopitalo-Universitaire, University of Strasbourg, France

¹⁵Medical ICU, Médecine Intensive - Réanimation, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, INSERM UMR 1260, Regenerative Nanomedicine (RNM), FMTS, Université de Strasbourg, France.

¹⁶Faculté de Médecine, University of Strasbourg, Inserm U1110, Strasbourg, France,

## Acknowledgments

We acknowledge all the members of SELHVA (Service Expert de Lutte contre les Hépatites Virales d'Alsace) for their contribution to this research. In addition, we acknowledge the different medical teams: Francois DANION, Yves HANSMANN, Charlotte KAEUFFER, Yvon RUCH (Department of Infectious Disease), Murielle RONDEAU-LUTZ, Juliette JEANNEL, Cornelia KUHNERT, Esther NOEL, Philippe MERTZ, Jean-Christophe WEBER (Department of Internal Medicine, Nouvel Hôpital Civil), Abrar-Ahmad ZULFIQAR, Hanane SALEM (Department of Internal Medicine, Medical clinic B, Nouvel Hôpital Civil), Samira FAFI-KREMER (Virology Laboratory), Bernard GOICHOT (Department of Internal Medicine, Diabetes and Metabolic Disorders, Hôpital de Hautepierre), Frédéric de BLAY (Department of Pneumology), Gérald ROUL (Departement of Cardiology), Thierry MARTIN (Department of Rheumatology), Thierry HANNEDOUCHE (Department of Nephrology), the physicians in charge of patients at the Emergency Room, and all the departments at the Hôpitaux Universitaires de Strasbourg that were in charge of patients with SARS-CoV-2 infection.
Moreover, we acknowledge all health-care workers involved in the diagnosis and treatment of COVID-19 patients, as well as the general management of the hospital for the organization of care in Hôpitaux Universitaires de Strasbourg. We thank all patients involved in the study.

## Authors' contributions:

ST conceived and designed the study, collected patient data, performed data analysis and interpretation, and drafted the manuscript. MD conceived and designed the study, analysis, and interpretation of the manuscript and contributed to the critical revision of the manuscript. FC, JM contributed to data collection. TFB, DM, FM contributed to the critical
revision of the manuscript. TF performed statistical analysis. All the others contributed to COVID-19 recruitment. All authors contributed to the manuscript revision and approved the submitted final version.

**Abbreviations:**

Alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), body mass index (BMI), coronavirus disease (COVID-19), fibrosis-4 (FIB-4), gamma-glutamyl transferase (GGT), hepatic steatosis index (HSI), liver function tests (LFTs), non-alcoholic fatty liver disease (NAFLD), NAFLD fibrosis score (NFS), capillary oxygen saturation (SaO2), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), total bilirubin (TBIL), prothrombin time (TP), upper limit of the normal (ULN).
**Abstract**

**Background and aims:** Coronavirus disease 2019 (COVID-19) is a serious public health issue that became rapidly pandemic. Liver injury and comorbidities, including metabolic syndrome, are associated with severe forms of the disease. This study sought to investigate liver injury, clinical features, and risk factors in patients with mild, moderate, and severe COVID-19.

**Methods:** We retrospectively included all consecutive patients hospitalized with laboratory-confirmed COVID-19 between February, 22 and May 15, 2020 at the emergency rooms of a French tertiary hospital. Medical history, symptoms, biological and imaging data were collected.

**Results:** Among the 1,381 hospitalizations for COVID-19, 719 patients underwent liver tests on admission and 496 (68.9%) patients displayed abnormal liver tests. Aspartate aminotransferase was most commonly abnormal in 57% of cases, followed by gamma-glutamyl transferase, alanine aminotransferase, albumin, alkaline phosphatase, and total bilirubin in 56.5%, 35.9%, 18.4%, 11.4%, and 5.8%. The presence of hepatocellular type more than 2xULN was associated with a higher risk of hospitalization and a worse course of severe disease (odd ratio [OR] 5.599; 95%CI: 1.27–23.86; p=0.021; OR 3.404; 95% CI: 2.12–5.47; p <0.001 respectively). A higher NAFLD fibrosis score was associated with a higher risk of hospitalization (OR 1.754; 95%CI: 1.27–2.43, p<0.001). **In multivariate analyses, patients with high fibrosis-4 index had a 3-fold greater risk of severe disease (p <0.001).**

**Conclusion:** Abnormal liver tests are common in patients with COVID-19 and could predict the outcome. Patients with non-alcoholic fatty liver disease and liver fibrosis are at higher risk of progressing to severe COVID-19.

**Key words:** SARS-CoV-2 infection, COVID-19 disease, liver function tests (LFTs), obesity, liver steatosis, liver fibrosis.
## Introduction

Coronaviruses are enveloped viruses that display the largest genome of all RNA viruses known to cause respiratory infections in humans (1). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the novel coronavirus disease that first occurred in 2019 (COVID-19) and became a serious global public health problem, declared by World Health Organization (WHO) as a global public health emergency. It can cause respiratory, intestinal, hepatic, and neuronal disease and may lead to acute respiratory distress syndrome, multiple organ failure, and death in severe cases (2).

As this infectious disease continues to spread, more information is needed to better identify the patients who require hospitalization or those who are likely develop severe COVID-19 disease. The identification of risk factors associated with outcomes should allow for patient stratification in view of personalized clinical care, resulting in outcome improvement.

Liver impairment has been commonly reported in COVID-19 patients (3). Abnormal liver function tests (LFTs) are present on admission, prior to initiating treatment with hepatotoxic drugs that may induce liver injury (4). However, the association between LFTs and different degrees of COVID-19 severity of is still poorly described.

The features of metabolic syndrome, including obesity, diabetes, and hypertension, are associated with a severe COVID-19 course (5). Non-alcoholic fatty liver disease (NAFLD) is associated with these metabolic comorbidities. The association and evolution of SARS-CoV-2 infection in patients with a preexisting liver disease like NAFLD are still incompletely understood.

The current study aimed to investigate liver injury, clinical features, and risk factors in patients with associated mild, moderate, and severe COVID-19.

## Patients and Methods

### Study design and participants

We retrospectively included all consecutive patients hospitalized for SARS-CoV-2 infection between February, 22 and May, 16, 2020 at the emergency rooms (ERs) of two French tertiary hospitals (Nouvel Hôpital Civil and Hôpital de Hautepierre - University Hospital of
Strasbourg). The study protocol was approved by the Ethics Committee of Strasbourg
University Hospital (CE-2020-138-26/08/2020).

Data Collection

The data collected for the hospitalized patients suffering from SARS-CoV-2 infection included their epidemiological and medical history, clinical signs, and biological and imaging data only at admission.

Three groups of patients were defined according to the hospitalization: (1) **mild disease**: patients hospitalized at the emergency room (ER) and discharged from the hospital without any indication of admission to a conventional hospitalization unit (CHU) or intensive care unit (ICU); (2) **moderate disease**: patients admitted to the ER and hospitalized thereafter in a CHU without signs of severe disease that would need intensive care support; (3) **severe disease**: patients needing intensive care support on admission or during the hospitalization in a CHU, patients hospitalized in the ICU, and patients who died during hospitalization in a CHU.

Liver tests were considered abnormal if the liver enzyme values, i.e., aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), and total bilirubin (TBIL), were elevated according to our laboratory upper limit of normal (ULN). Different patterns were defined: hepatocellular, cholestatic, and mixed patterns. The hepatocellular pattern was considered if AST or ALT were elevated higher than ULN; the cholestatic pattern if GGT or PAL were elevated up to ULN; the mixed pattern was a combination of the first two ones.

The exclusion criteria were described in the figure 1, as follows: patients with multiple hospitalizations for COVID-19, hospitalization for a pathology that may disturb LFTs (liver metastasis, cholangitis, cholecystitis, pancreatitis), patients transferred to other hospitals in Strasbourg or elsewhere, lost to follow-up or with missing clinical and biological data, patients with underlying chronic liver disease (viral, alcohol, autoimmune). Patients with diagnosed NAFLD were included in the study.
Moreover, NAFLD was evaluated using the hepatic steatosis index (HSI = 8 × [ALT/AST] + body mass index [BMI] [+ 2 if type 2 diabetes yes, + 2 if female]) >36 points (6) **and the severity related to the stage fibrosis with NAFLD fibrosis score (NFS)** (7). This HSI has been validated in COVID-19 patients to evaluate NAFLD (8-10). **The fibrosis was also evaluated using fibrosis-4 index (FIB-4): low (<1.30), intermediate (1.30-2.67), and high (>2.67)** (11). To evaluate the severity of the metabolic liver disease, we analyzed the different FIB-4 scores in patients diagnosed with NAFLD according to the HSI score.

Quantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) tests for SARS-CoV-2 nucleic acid were performed on admission with nasopharyngeal swabs. The primer and probe sequences targeted two regions on the RdRp gene and were specific to SARS-CoV-2. The assay sensitivity was around 10 copies/reaction (in-house method, *Institut Pasteur*, Paris, France) (12).

Chest computer tomography (CT) imaging was performed on admission for each patient. The images were classified based on the percentage of the whole lung parenchyma affected by COVID-19 in the following six groups: normal (no lesion), minimal (0%–10%), moderate (11%–25%), important (26%–50%), severe (51%–75%), and critical (>75%) (13). To simplify clinical data analysis, the patients were divided into three subgroups: ≤25%, 26%–50%, and >50%, according to their lesion extension.

## Statistics

The variables collected were expressed as the numbers and frequencies for qualitative variables (ordinal and categorical) and minimum, maximum, median, mean, and standard deviation for quantitative variables (discrete and continuous). These data were summarized in the descriptive phase.

For the bivariate analysis between groups, Fisher's exact test or Chi-squared tests were applied to compare qualitative variables, and Wilcoxon or Student t-tests were employed to compare quantitative variables.

To analyze the primary outcome, logistic regression was carried out by selecting variables from the literature, performing bivariate analysis, and then further selecting using Akaike information criterion (AIC).
Missing data were imputed by multiple imputations if they did not concern more than 30% of the data available; otherwise, the variable was excluded from the model.

The alpha risk was set at 5%, and all analyses were performed using R software, Version 4.0.

A multivariate analysis was performed after adjusting for confounding factors that might disturb the LFTs on admission, such as hypoxemia (capillary oxygen saturation [SaO₂] <93%, related to severe pneumopathy) (14) and hepatotoxic drugs (acetaminophen; antibiotics) administered prior to hospitalization. Hypotension, defined by systolic blood pressure <90mmHg, was not considered because of the low number of cases concerned (<10).

## Results

Between February, 22 and May, 15, 2020, a total of 1,381 patients with SARS-CoV-2 infection were admitted to the ERs. After excluding 662 patients according to our exclusion criteria, 719 were included in the subsequent analyses, as presented in the study flowchart (Figure 1).

## Characteristics of COVID-19 patients at baseline

According to COVID-19 severity, 101(14%) patients displayed mild disease, 393 (54.7%) presented with moderate disease, and 225 (31.3%) developed severe disease. The mean hospitalization length was 11.7 ± 12.8 days.

The mean age was 64.7 (19-97) years old, and 387 (58.8%) patients were men. Comorbidities were present in 513 (71.3%) patients, and the most common were BMI>25Kg/m² in 479 (70.6%) and BMI>30Kg/m² in 240 (33.3%), hypertension in 386 (53.7%), dyslipidemia in 226 (31.5%), diabetes in 201 (28%), chronic cardiac disease in 232 (32.3%), chronic respiratory disease in 176 (24.5%), chronic renal disease in 59 (8.2%), and cancer in 32 (4.5%).

The mean time between symptom onset and hospitalization was 7.2±6.1 days. The most common symptoms were flu-like syndrome (fever, fatigue, myalgia, chills, or headache) in 663 (92.2%) patients, respiratory manifestations (dyspnea, cough, or thoracic pain) in 701 (97.6%), digestive symptoms (loss of appetite, diarrhea, nausea, vomiting, or abdominal pain) in 344 (47.8%), neurological symptoms (anosmia, dysgeusia, vertigo, or confusion) in 223 (31%), and associated headache in 335 (46.6%).
Liver tests were performed on admission. The frequency of different abnormal LFTs, such as AST, ALT, ALP, GGT, TBIL, albumin, and prothrombin time (PT), was 57%, 35.9%, 11.4%, 56.5%, 5.8%, 18.4%, and 9.1%, respectively, while the mean values for these parameters were 53.8 (±85.1) u/L, 46.2 (±67.6) u/L, 74.2 (±39.1) u/L, 84.4 (±104.4) u/L, 11.3 (±14.5) µmol/L, 38.2 (±5.5) g/dL, and 87.8% (±14.7), respectively.

The LFTs exhibited a hepatocellular pattern in 60.9%, cholestatic pattern in 63.1%, and mixed-type pattern in 46.8% of patients.

The mean values of the inflammatory parameters were C-reactive protein (CRP) at 100.1(±84.8) mg/L, leucocytes at 7.4 (±3.8) * 10⁹/L, fibrinogen at 6.3(±1.6) g/L, and lymphocytes at 1.2(±1.3) * 10⁹/L.

An imaging pulmonary CT scan was performed on admission. Approximately 92% of patients displayed pneumopathy, with 45.7% having less than 25% of parenchyma affected, 31.1% between 26% and 50%, while in the remaining 14.1% of patients, the lesions affected over 50% of parenchyma.

## Comparison between mild and moderate COVID-19 on admission

Table 1 summarizes the clinical and biological characteristics on admission for patients with mild and moderate disease.

LFT laboratory data showed higher elevations of AST (p<0.001), ALT (p=0.05), and GGT (p=0.004) in addition to lower albumin (p<0.001) and PT (p=0.042) in patients with moderate versus mild COVID-19 (Table 1). AST levels were higher in patients hospitalized in a CHU compared to those in ER: 1-2xULN (p=0.001) and >2xULN (p=0.001).

Patients with moderate COVID-19 exhibited elevated GGT levels compared to mild disease, 53.6% vs 16.7% (p=0.016) respectively and more disturbed hepatocellular (p=0.002), cholestatic (p=0.004) and mixed-type (0.004) patterns. These patients had also higher NFS (p<0.001) and more severe liver fibrosis than patients with mild COVID-19: 71.7% had an intermediate/high FIB-4 fibrosis score compared to 41.6% with mild disease (p<0.001).

Patients with moderate disease had more NAFLD with an intermediate/high FIB-4 score compared to patients with mild disease (p<0.001) (Table 1).
As can be seen in Table 2, after adjusting for confounding factors that could disturb LFTs, such as hypoxemia and hepatotoxic drugs, patients with AST 2xULN and hepatocellular pattern 2xULN had a 5.5-fold higher risk of being hospitalized (OR 5.599; 95% CI: 1.29–24.25, p=0.021 and OR 5.499; 95%CI: 1.27–23.86; p=0.023, respectively). Patients with metabolic syndrome (the association of 3 metabolic risk factors (MRF)) had more than 2.5-fold higher risk of hospitalization (OR 2.56; 95%CI: 1.292–5.09, p=0.007).

Moderate disease was associated with an intermediate (OR 2.405; 95%CI: 1.39–4.15; p=0.002) and high (OR 5.415; 95%CI: 2.69–10.89; p< 0.001) FIB-4 score. Similarly, patients with an elevated NFS had a higher risk of hospitalization (OR 1.754 95%CI: 1.27–2.43; p<0.001).

In multivariate analyses, patients with metabolic syndrome had almost a 2.4-fold risk of being hospitalized (OR 2.37; 95% CI: 1.18–4.75, p=0.015).

## Comparison between mild/moderate and severe COVID-19

We compared the clinical and laboratory tests and radiological findings in patients with severe versus those with moderate and mild COVID-19 disease (Table 3).

The laboratory liver tests showed more elevated AST (p<0.001), ALT (p=0.002), and TBIL levels (p=0.01), in addition to lower albumin (p<0.001) and PT (p<0.001) in severe disease patients (Table 3).

Concerning abnormal LFT patterns, severe COVID-19 was associated with all types of patterns: hepatocellular (p<0.001), cholestatic (p=0.028), and mixed (p<0.001).

In severe disease patients, 89.3% had intermediate and high FIB-4 scores compared to 65.5% of patients with moderate and mild disease (p<0.001). More patients with severe COVID-19 had NAFLD associated with a high FIB-4: 72.8% vs. 57.9%, respectively (p<0.001) (Table 3).

After adjusting for confounding factors, as described, abnormal LFTs at admission such as AST>2x ULN (OR 3.187; 1.99–5.08; p< 0.001), hepatocellular pattern >2x ULN (2.265; 95% CI 1.48–3.45; p< 0.001), intermediate and high FIB-4 score (OR 2.006; 95%CI 1.09–3.67; p=0.024 and OR 4.756; 95%CI: 2.68–8.43; p<0.001 respectively) or metabolic syndrome (OR
2.45, 95%CI1.07-5.61, p=0.03) were associated with a worse evolution of the disease (Table 4).

In multivariate analyses, patients with high FIB-4 index had 3-fold risk to develop a severe disease (OR 3.11; 95%CI 1.4-6.91, p=0.005).

Discussion

Coronavirus disease 2019, the pandemic caused by SARS-CoV-2, has been associated with substantial morbidity and mortality (15). More information concerning SARS-CoV-2 infection is needed to rapidly identify patients likely to develop severe disease, requiring adjusted care. This is the first monocentric study that describes the outcome of a large cohort of COVID-19 patients based on clinical, laboratory, and radiological data collected at admission, enabling us to rapidly identify factors associated with outcomes that predict mild, moderate, or severe disease at the time of hospital admission the hospital.

Recently published studies have shown that patients admitted for COVID-19 often present with abnormal liver test, the incidence of liver injury ranging from 14% to 78% (1, 6, 16). Moreover, preexisting liver disease has been shown to be associated with worsening infection prognosis (17). However, the mechanistic events remain to be determined, and the causes of LFTs may be multifactorial, including causes unrelated to COVID-19. In contrast to previous studies, we analyzed only the laboratory liver tests at baseline in order to avoid the anomalies induced by different drugs administrated during the hospitalization.

In this present cohort, on admission, abnormal LFTs were present in 68.9% of patients, being more elevated than those previously reported in China at 14.9%, yet similar to those estimated in the United States at between 40%–67.5%. This controversy could be explained by the higher proportion of obese and diabetic patients in occidental countries (18).

Hepatocellular parameters, such as the AST and ALT, were elevated in 57% and 35.9% of patients, respectively, being beyond 2xULN in 17.4% and 4%, respectively. The patients with severe disease had a higher AST compared with moderate and mild disease patients (p<0.001). The ALT was also more elevated in severe COVID-19 patients when compared with the other patient groups (p=0.02). After adjusting for confounding factors, elevated
transaminases, such as AST >2xULN, and hepatocellular pattern type >2xULN were associated with a 3-fold (p<0.001) and almost 3.5-fold (p<0.001) higher risk, respectively, of a poorer clinical outcome.

Recent studies have shown that SARS-CoV-2 binds angiotensin converting enzyme 2 (ACE2) receptors localized both in the liver and bile duct cells. The expression is higher in the cholangiocytes that may be associated with cholestatic abnormalities (elevated ALP and GGT) rather than hepatocellular abnormalities (AST and ALT) (1, 20).

In our study, the ALP, GGT, and TBIL were elevated in 11.4%, 56.5%, and 5.8% of patients, respectively. Only an abnormal GGT was associated with moderate disease (GGT ULN, OR 0.108; 95%CI: 0.013–0.89, p=0.039). Interestingly, ALP >1–2xULN was more common in moderate and mild disease (91.5%) than in severe disease (83.9%) (p=0.045). Our data confirm the hypothesis of Hundt et al. reporting that the mechanism of liver damage may be not caused by direct cytopathic effects of the novel coronavirus (19).

In recent studies, obesity was associated with COVID-19 disease severity (21, 22). Indeed, in our study, 72.5% of patients that required hospitalization were overweight, with 34.6% being obese. The proportion of overweight patients increased with disease severity, as follows: 58.4% in mild, 71.8% in moderate, and 73.3% in severe COVID-19. Moreover, the hospitalized patients had a higher BMI compared to those discharged from an ER: 28.9 vs. 26.9Kg/m² (p=0.001).

A strong association between obesity and the severity of COVID-19 was previously reported, even in the absence of other comorbidities, yet the underlying mechanism is unclear (23).

Obesity is characterized by a chronic inflammatory state and disrupted immune response. The main cytokines responsible for chronic inflammation are tumor necrosis factor-alpha (TNF-α), interleukin (Il)-6, and IL-1 beta(β) (5). Moreover, SARS-CoV-2 may cause a hyper-inflammatory reaction through the excessive release of cytokines, a condition known as “cytokine storm,” that may be enhanced by obesity, particularly in patients with a preexisting liver disease like NAFLD (24). Adipose tissue may also be a viral reservoir and an immunological hub for the inflammatory response (25). Individuals with severe obesity, diabetes, and hypertension are more likely to be infected and are at a higher risk of complications and death due to COVID-19 (2).
Indeed, in our study, metabolic factors were associated with the severity of COVID-19.

Indeed, hypertension (p=0.002) and dyslipidemia (p=0.001) were associated with moderate
disease, and diabetes was associated with a severe course of the disease (p=0.032). Patients
with metabolic syndrome had a 2.5-fold higher risk to develop a moderate (p=0.007) or a
severe disease (p=0.033).

Abnormal GGT is commonly observed in NAFLD and is a sensitive marker of steatosis (11). In
our study, the ALP was elevated only in 11.4% of patients, and the mean values were not
significantly higher in mild disease compared to moderate (p=0.938) and severe forms
(p=0.178). Moreover, 56.5% of patients had abnormal GGT levels, and this was correlated
with the severity of COVID-19 when comparing severe versus moderate (p=0.02) and mild
disease forms (p=0.016).

The prognosis of liver mortality was related to liver fibrosis (26). Liver fibrosis evaluated with the FIB-4 score was associated with the severity of COVID-19 illness: 71.7% of patients with intermediate/high FIB-4 scores needed hospitalization compared to 41.6% of those with only mild disease (p<0.001), and it was higher in patients with severe SARS-COV-2 infection than in those with moderate and mild disease (89.3% compared to 65.5%, respectively) (p<0.001). Patients who needed hospitalization had a more elevated NFS compared to those who were discharged from an ER (p=0.001).

We associated the FIB-4 scores in patients diagnosed with NAFLD (Tables 2 and 4). In the hospitalized patients, 84.2% had an intermediate/high FIB-4 compared to 67.3% of those discharged from an ER (p<0.001). In patients with a high FIB-4, 72.8% had severe disease compared to the 57.9% of those with moderate and mild disease (p<0.001). Moreover, the patients with NAFLD without fibrosis (FIB-4<1.3) were at a lower risk of hospitalization (OR 0.404; 95%CI: 0.20-0.81; p<0.011).

In the multivariate-adjusted analysis, patients with high FIB-4 scores had an almost 3-fold greater risk of hospitalization (p=0.011), and a worse course of disease (p<0.001).

In the context of the COVID-19 pandemic, along with the high prevalence of NAFLD in the
general population, it is mandatory to early identify patients at risk of developing a severe
disease. Applying non-invasive tests such as the FIB-4 and NFS in patients with metabolic
liver disease could enable us to better and more quickly identify these patients that may
have a poorer prognosis (27).

Our study had some limitations. First, the data were collected retrospectively, and there was
missing information regarding the tobacco and alcohol consumption, several clinical
parameters (waist and hip circumferences), and laboratory tests pertaining to metabolic
syndrome, including glucose, glycated hemoglobin, cholesterol, and triglycerides levels.
Second, while all patients underwent transaminase measurements at admission, only half of
them underwent cholestasis enzyme assessment at admission, which could be deemed as a
limitation in the statistical analysis. **Third, HIS index was used in several publications to
define NAFLD in COVID 19 patients (8-10). It is a simple, disponible score that may help us to
easy identify high-risk patients. His limitation is related to the elevated transaminases levels
that could underestimate the index.**

In conclusion, obese patients with metabolic syndrome and a preexisting liver disease,
including NAFLD and liver fibrosis, display a dysregulated hepatic innate immunity and pro-
inflammatory state that is likely to enhance the “cytokine storm” due to SARS CoV-2
infection, leading to severe COVID-19 disease. Thus, abnormal LFTs at admission predict the
outcome of COVID-19 patients.

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<caption>Figure 1. Flowchart of patients included in the study</caption>
<figure><img src="image_4.png" /><figcaption>Table 1. Characteristics of patients with mild and moderate COVID-19 on admission</figcaption></figure>
<table><thead><tr><th>Variable</th><th>Mild COVID-19 (n=101)</th><th>Moderate COVID-19 (n=393)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years, SD*)</td><td>57.7 (18.4)</td><td>65.7 (7)</td><td>&lt;0.001</td></tr><tr><td>Gender (male, %)</td><td>52 (51.4)</td><td>198 (50.4)</td><td>0.468</td></tr><tr><td>BMI (kg/m², SD)</td><td>26.9 (4.9)</td><td>28.8 (5)</td><td>0.004</td></tr><tr><td><strong>Comorbidity</strong></td><td></td><td></td><td></td></tr><tr><td>Diabetes (n, %)</td><td>21 (20.8)</td><td>105 (26.7)</td><td>0.251</td></tr><tr><td>Hypertension (n, %)</td><td>38 (37.6)</td><td>217 (55.2)</td><td>0.002</td></tr><tr><td>Dyslipidemia (n, %)</td><td>17 (16.8)</td><td>129 (32.8)</td><td>0.001</td></tr><tr><td>Chronic pulmonary disease (n, %)</td><td>19 (18.8)</td><td>94 (24)</td><td>0.291</td></tr><tr><td>Chronic cardiac disease (n, %)</td><td>19 (18.8)</td><td>106 (27)</td><td>0.097</td></tr><tr><td>Chronic kidney disease (n, %)</td><td>4 (4)</td><td>26 (6.6)</td><td>0.482</td></tr><tr><td>Cancer (n, %)</td><td>2 (2)</td><td>20 (5.1)</td><td>0.277</td></tr><tr><td><strong>Symptoms onset</strong></td><td></td><td></td><td></td></tr><tr><td>“Flu-like” symptoms (n, %)</td><td>87 (86.1)</td><td>366 (93.1)</td><td>0.041</td></tr><tr><td>Respiratory symptoms (n, %)</td><td>87 (86.1)</td><td>351 (89.3)</td><td>0.014</td></tr><tr><td>Digestive symptoms (n, %)</td><td>40 (39.6)</td><td>208 (52.9)</td><td>0.019</td></tr><tr><td>Neurological symptoms (n, %)</td><td>45 (44.6)</td><td>184 (46.8)</td><td>0.738</td></tr><tr><td><strong>Clinical features</strong></td><td></td><td></td><td></td></tr><tr><td>Oxygen saturation SaO2 (%, SD)</td><td>96 (3.9)</td><td>92.1 (5.9)</td><td>&lt;0.001</td></tr><tr><td>Debit of oxygen at admission (L/mn, sd)</td><td>0.4 (1.8)</td><td>2.3 (7.4)</td><td>&lt;0.001</td></tr><tr><td>Systolic arterial pressure (mmHg, SD)</td><td>132.4 (17.9)</td><td>133.1 (25.8)</td><td>0.683</td></tr><tr><td><strong>Laboratory tests</strong></td><td></td><td></td><td></td></tr><tr><td>AST U/L (reference range 11-34)</td><td>30.4 (20.4)</td><td>45.5 (36.2)</td><td>&lt;0.001</td></tr><tr><td>ALT U/L (reference range 8-41)</td><td>35.7 (27.5)</td><td>42.3 (34)</td><td>0.05</td></tr><tr><td>ALP U/L (reference range 41-117)</td><td>66.1 (13.2)</td><td>76.1 (40.6)</td><td>0.938</td></tr><tr><td>GGT U/L (reference range 6-40)</td><td>30.2 (23.7)</td><td>87.6 (119.2)</td><td>0.004</td></tr><tr><td>Bilirubin µmol/L, (reference range 1.7-21.0)</td><td>9.1 (4.9)</td><td>11.3 (18.3)</td><td>0.279</td></tr><tr><td>Albumin g/L, (reference range 35-50)</td><td>43.1 (3.9)</td><td>39.2 (5)</td><td>&lt;0.001</td></tr><tr><td>Abnormality type (n, %)</td><td></td><td></td><td></td></tr><tr><td>Hepatocellular &gt; ULN</td><td>39 (38.6)</td><td>221 (56.2)</td><td>0.002</td></tr><tr><td>Cholestatic &gt; ULN</td><td>2 (16.7)</td><td>112 (61.2)</td><td>0.004</td></tr><tr><td>Mixed &gt; ULN</td><td>0 (0)</td><td>72 (39.3)</td><td>0.004</td></tr><tr><td>PT (normal range &gt;70%) (%, SD)</td><td>92.6 (8)</td><td>87.9 (15.7)</td><td>0.042</td></tr><tr><td>CRP mg/L (reference range &lt; 4)</td><td>36.1 (54.1)</td><td>87.9 (70.5)</td><td>&lt;0.001</td></tr><tr><td>Leucocytes *10<sup>9</sup>/L (reference range 4.5-6.5)</td><td>6.2 (2.6)</td><td>7(3.4)</td><td>0.02</td></tr><tr><td>Neutrophil count *10<sup>9</sup>/L (reference range 1.3-7.7)</td><td>4.2 (2.2)</td><td>5.3 (3)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L (reference range 1.0-4.0)</td><td>1.5 (0.8)</td><td>1.2 (1.3)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L ≤1.0 (n, %)</td><td>31 (30.7)</td><td>200 (50.9)</td><td>0.001</td></tr><tr><td>Fibrinogen g/L (reference range: 2-4)</td><td>5(1.4)</td><td>5.9 (1.5)</td><td>0.016</td></tr></tbody></table>
<table><tr><td>Platelets *10<sup>9</sup>/L<br>(reference range: 150-400)</td><td>236.2 (104.6)</td><td>226.3 (99.8)</td><td>0.331</td></tr><tr><td>D-Dimers (reference range: 150-400)</td><td>765 (698)</td><td>2452.9 (4111.5)</td><td>0.05</td></tr><tr><td><strong>Metabolic and fibrosis characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>FIB-4 status (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>&lt;1.3</td><td>59 (58.4)</td><td>111 (28.3)</td><td></td></tr><tr><td>1.3-2.67</td><td>29 (28.7)</td><td>142 (36.2)</td><td></td></tr><tr><td>&gt;2.67</td><td>13 (12.9)</td><td>139 (35.5)</td><td></td></tr><tr><td>NAFLD (n, %)</td><td>62 (61.4)</td><td>249 (63.4)</td><td>0.73</td></tr><tr><td>NAFLD+ et FIB-4 (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>NAFLD + FIB-4 &lt;1.3</td><td>33 (32.7)</td><td>62 (15.8)</td><td></td></tr><tr><td>NAFLD + FIB-4 1.3-2.67</td><td>19 (18.8)</td><td>94 (23.9)</td><td></td></tr><tr><td>NAFLD + FIB-4 &gt;2.67</td><td>49 (48.5)</td><td>237 (60.3)</td><td></td></tr><tr><td>NFS (mean, SD)</td><td>-2 (1.5)</td><td>-0.7 (1.7)</td><td>&lt;0.001</td></tr><tr><td><strong>Radiologic findings</strong></td><td></td><td></td><td></td></tr><tr><td>Severity of pneumonia (CT scan)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>-absence</td><td>25 (26%)</td><td>24 (6.8%)</td><td></td></tr><tr><td>-&lt;25%</td><td>58 (60.4%)</td><td>185 (52.6%)</td><td></td></tr><tr><td>25-50%</td><td>10 (10.4%)</td><td>116 (33%)</td><td></td></tr><tr><td>&gt;50%</td><td>3 (3.1%)</td><td>27 (7.7%)</td><td></td></tr><tr><td>Delays from symptom onset to<br>hospitalization (days, SD)</td><td>7.2 (6.8)</td><td>7.6 (6.8)</td><td>0.592</td></tr><tr><td>Time of hospitalization (days, SD)</td><td>1 (0.9)</td><td>10.6 (8.5)</td><td>&lt;0.001</td></tr></table>

BMI: body mass index

SD: standard deviation

COVID-19: coronavirus disease 2019

AST: aspartate aminotransferase

ALT: alanine aminotransferase

ALP: alkaline phosphatase

ULN: upper limit of the normal

GGT: gamma-glutamyl transferase

LFTs: liver function tests

PT: prothrombin time

FIB-4: fibrosis-4

CRP: Creatine-reactive protein

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2

CT: computed tomography

HSI: hepatic steatosis index

NAFLD: non-alcoholic fatty liver disease

NFS: NAFLD fibrosis score

SaO2: capillary oxygen saturation

TBIL: total bilirubin
<table><caption>Table 2. Factors associated with moderate COVID-19, after adjusting for confounding factors</caption><thead><tr><th></th><th>OR</th><th>95%CI</th><th>p-value</th></tr></thead><tbody><tr><td>MS (MRF 1+2+3)</td><td>2.85</td><td>1.49-5.45</td><td>0.002</td></tr><tr><td>MS (MRF 1+2+4)</td><td>2.56</td><td>1.29-5.09</td><td>0.007</td></tr><tr><td>MS (MFR 1+3+4)</td><td>3.09</td><td>1.29-6.89</td><td>0.006</td></tr><tr><td>NFS score</td><td>1.754</td><td>1.27-2.43</td><td>&lt;0.001</td></tr><tr><td>FIB-4 (1.3-2.67)</td><td>2.405</td><td>1.39-4.15</td><td>0.002</td></tr><tr><td>FIB-4 &gt;2.67</td><td>5.415</td><td>2.69-10.89</td><td>&lt;0.001</td></tr><tr><td>NAFLD+ and FIB-4&lt;1.30</td><td>0.404</td><td>0.20-0.81</td><td>0.011</td></tr><tr><td>ASTULN</td><td>0.395</td><td>0.23-0.66</td><td>&lt;0.001</td></tr><tr><td>AST&gt;2 ULN</td><td>5.599</td><td>1.29-24.25</td><td>0.021</td></tr><tr><td>Hepatocellular type &gt;2 ULN</td><td>5.499</td><td>1.27-23.86</td><td>0.023</td></tr><tr><td>GGT ULN</td><td>0.108</td><td>0.01-0.89</td><td>0.039</td></tr><tr><td>Cholestatic type &gt;ULN</td><td>11.915</td><td>1.45-98.27</td><td>0.021</td></tr><tr><td>Albumin</td><td>0.805</td><td>0.70-0.92</td><td>0.002</td></tr><tr><td>Neutrophil count</td><td>1.13</td><td>1.02-1.25</td><td>0.023</td></tr><tr><td>Lymphocytes&lt;1000/µL</td><td>2.196</td><td>1.33-3.64</td><td>0.002</td></tr><tr><td>Prothrombin time</td><td>0.963</td><td>0.94-0.99</td><td>0.01</td></tr><tr><td>CRP</td><td>1.017</td><td>1.01-1.02</td><td>&lt;0.001</td></tr><tr><td>CRP &lt;100 mg/L</td><td>0.29</td><td>0.14-0.62</td><td>&lt;0.001</td></tr></tbody></table>

OR: odd ratio; CI: confidence interval; MS: metabolic syndrome, MRF: metabolic risk factors: 1-obesity; 2-diabetes, 3-dyslipidemia, 4-hypertension.

Adjustment for confounding factors that could disturb LFTs, as follows: hypoxemia (SaO2 < 93% related to severe pneumopathy) (16); hepatotoxic drugs (acetaminophen, antibiotics) administrated before hospitalization. Hypotension defined by systolic blood pressure < 90mmHg was not considered because of low case number <10
<table><caption>Table 3. Characteristics of patients with mild and moderate versus severe COVID-19 on admission.</caption><thead><tr><th>Variable</th><th>Mild and<br>Moderate COVID-<br>19<br>(n=494)</th><th>Severe COVID-19<br>(n=225)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years, SD*)</td><td>63.1 (17.5)</td><td>68.3 (16.9)</td><td>&lt;0.001</td></tr><tr><td>Gender (male, %)</td><td>251 (50.8)</td><td>138 (61.3)</td><td>0.94</td></tr><tr><td>BMI (Kg/m²) (mean, SD)</td><td>28.4 (5.9)</td><td>28.9 (5.9)</td><td>0.255</td></tr><tr><td><strong>Comorbidity</strong></td><td></td><td></td><td></td></tr><tr><td>Diabetes (n, %)</td><td>126 (25.5)</td><td>75 (33.3)</td><td>0.032</td></tr><tr><td>Hypertension (n, %)</td><td>255 (51.6)</td><td>131 (58.2)</td><td>0.107</td></tr><tr><td>Dyslipidemia (n, %)</td><td>146 (29.6)</td><td>80 (35.7)</td><td>0.118</td></tr><tr><td>Chronic pulmonary disease (n, %)</td><td>113 (23)</td><td>63 (28)</td><td>0.161</td></tr><tr><td>Chronic cardiac disease (n, %)</td><td>125 (25.3)</td><td>72 (32)</td><td>0.071</td></tr><tr><td>Chronic kidney disease (n, %)</td><td>30 (6.1)</td><td>29 (12.9)</td><td>0.003</td></tr><tr><td>Cancer (n, %)</td><td>22 (4.5)</td><td>10 (4.5)</td><td>1</td></tr><tr><td><strong>Symptoms onset</strong></td><td></td><td></td><td></td></tr><tr><td>“Flu-like” symptoms (n, %)</td><td>453 (91.7)</td><td>210 (93.3)</td><td>0.549</td></tr><tr><td>Respiratory symptoms (n, %)</td><td>438 (88.7)</td><td>208 (92.4)</td><td>0.435</td></tr><tr><td>Digestive symptoms (n, %)</td><td>248 (50.2)</td><td>96 (42.7)</td><td>0.064</td></tr><tr><td>Neurological symptoms (n, %)</td><td>229 (46.4)</td><td>106 (47.1)</td><td>0.872</td></tr><tr><td><strong>Clinical features</strong></td><td></td><td></td><td></td></tr><tr><td>Oxygen saturation SaO2 (%, SD)</td><td>92.9 (5.7)</td><td>85.3 (9.1)</td><td>&lt;0.001</td></tr><tr><td>Debit of oxygen at admission (l/mn, sd)</td><td>1.9 (6.7)</td><td>6.5 (7.7)</td><td>&lt;0.001</td></tr><tr><td>Systolic arterial pressure, mmHg</td><td>132.5 (20.8)</td><td>131.5 (20.9)</td><td>0.375</td></tr><tr><td><strong>Laboratory tests</strong></td><td></td><td></td><td></td></tr><tr><td>AST U/L (reference range, 11-34)</td><td>48 (87.8)</td><td>73.1 (64.6)</td><td>&lt;0.001</td></tr><tr><td>ALT U/L (reference range, 8-41)</td><td>40.9 (32.8)</td><td>57.9 (110)</td><td>0.002</td></tr><tr><td>ALP U/L (reference range, 41-117)</td><td>75.2 (39)</td><td>72.3 (39.4)</td><td>0.178</td></tr><tr><td>GGT U/L (reference range, 6-40)</td><td>84.2 (116.5)</td><td>84.7 (79.6)</td><td>0.087</td></tr><tr><td>Total Bilirubin µmol/l (reference range, 1.7-21.0)</td><td>11.1 (17.5)</td><td>11.6 (6.9)</td><td>0.01</td></tr><tr><td>Conjugated Bilirubin µmol/l (reference range&lt; 4.3)</td><td>3.7 (2.6)</td><td>4.2 (2.4)</td><td>0.055</td></tr><tr><td>Albumin g/, (reference range, 35-50)</td><td>39.7 (5)</td><td>36.4 (5.4)</td><td>&lt;0.001</td></tr><tr><td>PT (normal range &gt;70%) (%, SD)</td><td>88.9 (14.6)</td><td>85.6 (14.7)</td><td>&lt;0.001</td></tr><tr><td><strong>Abnormality type (n, %)</strong></td><td></td><td></td><td></td></tr><tr><td>Hepatocellular &gt;ULN</td><td>260 (52.6)</td><td>178 (79.1)</td><td>&lt;0.001</td></tr><tr><td>Cholestatic &gt;ULN</td><td>114 (58.5)</td><td>81 (71.1)</td><td>0.028</td></tr><tr><td>Mixed type &gt;ULN</td><td>72 (36.9)</td><td>72 (63.7)</td><td>&lt;0.001</td></tr><tr><td>CRP mg/L (reference range&lt; 4)</td><td>77.7 (70.6)</td><td>148.7 (92.6)</td><td>&lt;0.001</td></tr><tr><td>Leucocytes *10<sup>9</sup>/l</td><td>6.9 (3.3)</td><td>8.6 (4.4)</td><td>&lt;0.001</td></tr></tbody></table>
<table><tbody><tr><td>(reference range 4.5-6.5)</td><td></td><td></td><td></td></tr><tr><td>Neutrophil count *10<sup>9</sup>/l<br>(reference range 1.3-7.7)</td><td>5.1 (2.9)</td><td>6.9 (3.9)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/l<br>(reference range: 1.0-4.0)</td><td>1.2 (1.2)</td><td>1 (1.5)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L<br>≤ 1.0 (n, %)</td><td>231 (46.8)</td><td>137 (60.9)</td><td>0.001</td></tr><tr><td>Fibrinogen g/l (reference range 2-4)</td><td>5.8 (1.5)</td><td>6.8 (1.4)</td><td>&lt;0.001</td></tr><tr><td>Platelets *10<sup>9</sup>/L<br>(reference range 150-400)</td><td>228.3 (99.8)</td><td>210.6 (5.4)</td><td>0.028</td></tr><tr><td>D-Dimers (reference range 150-400)</td><td>2302.9 (3956.9)</td><td>2849.6 (4072.5)</td><td>0.001</td></tr><tr><td><strong>Metabolic and fibrosis characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>FIB-4 score (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>≤1.3</td><td>170 (34.5)</td><td>24 (10.7)</td><td></td></tr><tr><td>1.3-2.67</td><td>171 (34.7)</td><td>67 (29.9)</td><td></td></tr><tr><td>&gt;2.67</td><td>152 (30.8)</td><td>133 (59.4)</td><td></td></tr><tr><td>NAFLD (n, %)</td><td>311 (63)</td><td>134 (59.8)</td><td>0.455</td></tr><tr><td>NAFLD+ et FIB-4 (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>NAFLD + FIB-4 &lt;1.3</td><td>95 (19.2)</td><td>20 (8.9)</td><td></td></tr><tr><td>NAFLD + FIB-4 1.3-2.67</td><td>113 (22.9)</td><td>41 (18.3)</td><td></td></tr><tr><td>NAFLD + FIB-4 &gt;2.67</td><td>286 (57.9)</td><td>163 (72.8)</td><td></td></tr><tr><td>NAFLD fibrosis score (mean, SD)</td><td>-0.8 (1.8)</td><td>-0.6 (1.6)</td><td>0.435</td></tr><tr><td><strong>Radiologic findings</strong></td><td></td><td></td><td></td></tr><tr><td>Severity of pneumonia (CT scan)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>-absence (n, %)</td><td>49 (10.9%)</td><td>10 (5%)</td><td></td></tr><tr><td>-&lt;25%</td><td>243 (54.2%)</td><td>53 (26.6%)</td><td></td></tr><tr><td>25-50%</td><td>126 (28.1%)</td><td>75 (37.7%)</td><td></td></tr><tr><td>&gt;50%</td><td>30 (6.7%)</td><td>61 (30.7%)</td><td></td></tr><tr><td>Delays from symptoms onset to<br>hospitalization (days, SD)</td><td>6.6 (6.2)</td><td>7.2 (5.3)</td><td>0.062</td></tr><tr><td>Time of hospitalization</td><td>8.7 (8.5)</td><td>19.1 (12.8)</td><td>&lt;0.001</td></tr></tbody></table>

SD, standard deviation

mean values

BMI: body mass index

SD: standard deviation

COVID-19: coronavirus disease 2019

AST: aspartate aminotransferase

ALT: alanine aminotransferase

ALP: alkaline phosphatase

ULN: upper limit of the normal

GGT: gamma-glutamyl transferase

LFTs: liver function tests

PT: prothrombin time
FIB-4: fibrosis-4

CRP: Creatine-reactive protein

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2

CT: computed tomography

HSI: hepatic steatosis index

NAFLD: non-alcoholic fatty liver disease

NFS: NAFLD fibrosis score

SaO2: capillary oxygen saturation

TBIL: total bilirubin

<table><caption>Table 4. Factors associated with moderate severe COVID-19 after adjusting for confounding factors.</caption><thead><tr><th></th><th>OR</th><th>95%CI</th><th>p-value</th></tr></thead><tbody><tr><td>MS (MRF 1+2+3+4)</td><td>2.45</td><td>1.07-5.61</td><td>0.033</td></tr><tr><td>NFS</td><td>1.181</td><td>1.01-1.37</td><td>0.033</td></tr><tr><td>FIB-4 (1.3-2.67)</td><td>2.006</td><td>1.09-3.67</td><td>0.024</td></tr><tr><td>FIB-4 > 2.67</td><td>4.756</td><td>2.68-8.43</td><td>&lt;0.001</td></tr><tr><td>AST ULN</td><td>0.406</td><td>0.27-0.61</td><td>&lt;0.001</td></tr><tr><td>AST >2 ULN</td><td>3.187</td><td>1.99-5.08</td><td>&lt;0.001</td></tr><tr><td>ALT ULN</td><td>0.716</td><td>0.48-1.05</td><td>0.09</td></tr><tr><td>Hepatocellular type>ULN</td><td>2.265</td><td>1.48-3.45</td><td>&lt;0.001</td></tr><tr><td>Hepatocellular type >2 ULN</td><td>3.404</td><td>2.12-5.47</td><td>&lt;0.001</td></tr><tr><td>GGT>1-3 ULN</td><td>1.676</td><td>0.97-2.88</td><td>0.062</td></tr><tr><td>Albumin ULN</td><td>0.422</td><td>0.21-0.82</td><td>0.01</td></tr><tr><td>Albumin</td><td>0.871</td><td>0.82-0.92</td><td>&lt;0.001</td></tr><tr><td>Leucocytes count</td><td>1.104</td><td>1.05-1.16</td><td>&lt;0.001</td></tr><tr><td>Neutrophil count</td><td>1.136</td><td>1.07-1.21</td><td>&lt;0.001</td></tr><tr><td>CRP >200 mg/L</td><td>2.785</td><td>1.66-4.66</td><td>&lt;0.001</td></tr><tr><td>CRP <100 mg/L</td><td>0.441</td><td>0.29-0.65</td><td>&lt;0.001</td></tr><tr><td>Fibrinogen</td><td>1.317</td><td>1.05-1.64</td><td>0.015</td></tr><tr><td>Lupus-anticoagulant</td><td>4.453</td><td>1.49-13.23</td><td>0.007</td></tr></tbody></table>

OR: odd ratio; CI: confidence interval; MS: metabolic syndrome, MRF: metabolic risk factors: 1-obesity; 2-diabetes, 3-dyslipidemia, 4-hypertension.

Adjustment for confounding factors that could disturb LFTs, as follows: hypoxemia (SaO2 < 93% related to severe pneumopathy) (16), and hepatotoxic drugs (acetaminophen, antibiotics) administrated before the hospitalization. Hypotension defined by systolic blood pressure <90mmHg was not considered because of the low number of cases <10.
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Reviewer #1: I appreciate the authors efforts to respond to reviewers comment.
Still I have some points that should be revised:

1. Page 7 line 6: "This HSI has been validated in COVID-19 patients to evaluate NAFLD ref 8-10" - please rephrase as these publications only used HSI to diagnose NAFLD (because no availability of other noninvasive methods to assign the diagnosis of NAFLD) but have no intention to validate it ...

**Modified in the text**

2. As suggested in my first review I think that it would be necessary to add a table 1 with all patients' characteristics before splitting patients in different groups according to the severity of COVID 19. This will allow to present some clinical characteristics - mean age, % of diabetes, obesity, HBP etc, as text and other like mean values of LFT and biological parameters in table

As the reviewer suggested in the first review << "Characteristics of COVID-19 patients at baseline" - this paragraph can be shortened; instead of presenting the results for different parameters as text, the authors can add a table (ex Table 1: patient's general characteristics at baseline) with general characteristics of all patients, we shortened the text and we keep only the information that could not be presented in tables. Clinical and biological data such as (mean age, gender, % of diabetes, obesity, hypertension, etc LFTs and date are presented already in tables 1 and 3 for each severity group of patients.

We included in the text the clinical characteristics for the patients as the reviewer suggested.

3. Respectfully, I do not agree with the authors answer "We presented the data in two separated tables because the primary and secondary endpoints were to define the impact of different factors (LFTs, NAFLD, the severity of liver fibrosis, etc) to the risk of hospitalization in conventional Units or ICU. We considered that making two tables allowed us to have a clearer response to these questions comparing easier 2 by 2 groups". 3 groups comparison can be made using ANOVA. Besides the groups presented in table 1 and 4 does not allow a 2 by 2 groups comparisons as in table 1 we have mild vs moderate and in table 3 mild and moderate are collapsed and compared with the severe group ...

We presented two tables for several reasons:

- the first table is in relation with the main outcome and the second with secondary outcome

- it is easier to represent and understand data when the grouping variable has only two modalities

- we could not do a "ANOVA" here because the populations used are not the same.

4. The authors' comment concerning the collinearity between GGT and non-invasive score of fibrosis/NAFLD used in multivariable models is not acceptable. "We agree with the reviewer that it could be a collinearity, but the endpoints of our study were to show that abnormal liver tests could predict the outcome and patients with nonalcoholic fatty liver disease and liver fibrosis are at higher risk of progressing to severe COVID-19. »
In non-invasives score of fibrosis/NAFLD, “GGT” is not included in these scores, neither in the FIB-4 index, nor the NAFLD fibrosis score.

Obese patients with metabolic syndrome and a preexisting liver disease such as NAFLD and liver fibrosis have already an inflammatory state that is likely to enhance the “cytokine storm” due to SARS CoV-2 infection that may lead to severe COVID-19 disease.

5. Need to revise the English

The manuscript was revised by “Cremer Consulting” SARL.
1) Abnormal liver tests at admission are associated with a poorer clinical outcome.

2) Patients with metabolic syndrome and NAFLD related fibrosis have a higher risk of hospitalization.

3) High FIB-4 index at admission may predict the risk to develop a moderate or severe disease.
Abnormal liver tests and non-alcoholic fatty liver disease predict disease progression and
outcome of patients with Covid-19

Simona TRIPON¹, Pascal BILBAULT², Thibaut FABACHER³, Nicolas LEFEBVRE⁴, Sylvain
LESCUYER⁵, Emmanuel ANDRES⁶, Elise SCHMITT⁷, Sabrina GARNIER-KEPKA⁸, Pierrick Le
BORGNE⁹, Joris MULLER¹⁰, Hamid MERDJI¹¹, Frédéric CHAFFRAIX¹², Didier MUTTER¹³, Thomas
F BAUMERT¹⁴, Ferhat MEZIANI¹⁵, and Michel DOFFOEL¹⁶

¹Department of Hepatology and Gastroenterology, Service Expert de Lutte contre les
Hépatites Virales d'Alsace (SELHVA), Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux
Universitaires de Strasbourg, Institut des Maladies Virales et Hépatiques, Inserm U1110,
Strasbourg, France.

² Head of Emergency Department, Hôpitaux Universitaires de Strasbourg, Regenerative Nanomedicine, INSERM UMR 1260 Fédération de Médecine Translationelle de Strasbourg, University of Strasbourg, France

³Department of Public Health, Hôpitaux Universitaires de Strasbourg, France

⁴Department of Infectious Diseases, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

⁵Internal Medicine Department, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

⁶Department of Internal Medicine, Medical Clinic B, Hôpitaux Universitaires de Strasbourg, University of Strasbourg, France

⁷Geriatric Department, Hôpitaux Universitaires de Strasbourg, EA-3072, University of Strasbourg, France

⁸Emergency Department, Hôpitaux Universitaires de Strasbourg, France

⁹Emergency Department, Hôpitaux Universitaires de Strasbourg, Regenerative Nanomedicine, INSERM UMR 1260 Fédération de Médecine Translationelle de Strasbourg, University of Strasbourg, France

¹⁰Department of Public Health, Hôpitaux Universitaires de Strasbourg, France

¹¹Medical ICU, Médecine Intensive- Réanimation, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, INSERM UMR 1260, Regenerative Nanomedicine (RNM), FMTS, Université de Strasbourg, France.

¹²Service Expert de Lutte contre les Hépatites Virales d'Alsace (SELHVA), Pôle Hépato-digestif, Nouvel Hôpital Civil, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

¹³Department of General, Digestive, and Endocrine Surgery, Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France
¹⁴Department of Hepatology and Gastroenterology, Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Institut des Maladies Virales et Hépatiques, Inserm U1110, Institut Hopitalo-Universitaire, University of Strasbourg, France

¹⁵Medical ICU, Médecine Intensive - Réanimation, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, INSERM UMR 1260, Regenerative Nanomedicine (RNM), FMTS, Université de Strasbourg, France.

¹⁶Faculté de Médecine, University of Strasbourg, Inserm U1110, Strasbourg, France,

## Acknowledgments

We acknowledge all the members of SELHVA (Service Expert de Lutte contre les Hépatites Virales d'Alsace) for their contribution to this research. In addition, we acknowledge the different medical teams: Francois DANION, Yves HANSMANN, Charlotte KAEUFFER, Yvon RUCH (Department of Infectious Disease), Murielle RONDEAU-LUTZ, Juliette JEANNEL, Cornelia KUHNERT, Esther NOEL, Philippe MERTZ, Jean-Christophe WEBER (Department of Internal Medicine, Nouvel Hôpital Civil), Abrar-Ahmad ZULFIQAR, Hanane SALEM (Department of Internal Medicine, Medical clinic B, Nouvel Hôpital Civil), Samira FAFI-KREMER (Virology Laboratory), Bernard GOICHOT (Department of Internal Medicine, Diabetes and Metabolic Disorders, Hôpital de Hautepierre), Frédéric de BLAY (Department of Pneumology), Gérald ROUL (Departement of Cardiology), Thierry MARTIN (Department of Rheumatology), Thierry HANNEDOUCHE (Department of Nephrology), the physicians in charge of patients at the Emergency Room, and all the departments at the Hôpitaux Universitaires de Strasbourg that were in charge of patients with SARS-CoV-2 infection.
Moreover, we acknowledge all health-care workers involved in the diagnosis and treatment of COVID-19 patients, as well as the general management of the hospital for the organization of care in Hôpitaux Universitaires de Strasbourg. We thank all patients involved in the study.

## Authors' contributions:

ST conceived and designed the study, collected patient data, performed data analysis and interpretation, and drafted the manuscript. MD conceived and designed the study, analysis, and interpretation of the manuscript and contributed to the critical revision of the manuscript. FC, JM contributed to data collection. TFB, DM, FM contributed to the critical

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revision of the manuscript. TF performed statistical analysis. All the others contributed to COVID-19 recruitment. All authors contributed to the manuscript revision and approved the submitted final version.

**Abbreviations:**

Alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), body mass index (BMI), coronavirus disease (COVID-19), fibrosis-4 (FIB-4), gamma-glutamyl transferase (GGT), hepatic steatosis index (HSI), liver function tests (LFTs), non-alcoholic fatty liver disease (NAFLD), NAFLD fibrosis score (NFS), capillary oxygen saturation (SaO2), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), total bilirubin (TBIL), prothrombin time (TP), upper limit of the normal (ULN).
**Abstract**

**Background and aims:** Coronavirus disease 2019 (COVID-19) is a serious public health issue that became rapidly pandemic. Liver injury and comorbidities, including metabolic syndrome, are associated with severe forms of the disease. This study sought to investigate liver injury, clinical features, and risk factors in patients with mild, moderate, and severe COVID-19.

**Methods:** We retrospectively included all consecutive patients hospitalized with laboratory-confirmed COVID-19 between February, 22 and May 15, 2020 at the emergency rooms of a French tertiary hospital. Medical history, symptoms, biological and imaging data were collected.

**Results:** Among the 1,381 hospitalizations for COVID-19, 719 patients underwent liver tests on admission and 496 (68.9%) patients displayed abnormal liver tests. Aspartate aminotransferase was most commonly abnormal in 57% of cases, followed by gamma-glutamyl transferase, alanine aminotransferase, albumin, alkaline phosphatase, and total bilirubin in 56.5%, 35.9%, 18.4%, 11.4%, and 5.8%. The presence of hepatocellular type more than 2xULN was associated with a higher risk of hospitalization and a worse course of severe disease (odd ratio [OR] 5.599; 95%CI: 1.27–23.86; p=0.021; OR 3.404; 95% CI: 2.12–5.47; p <0.001 respectively). A higher NAFLD fibrosis score was associated with a higher risk of hospitalization (OR 1.754; 95%CI: 1.27–2.43, p<0.001). In multivariate analyses, patients with high fibrosis-4 index had a 3-fold greater risk of severe disease (p <0.001).

**Conclusion:** Abnormal liver tests are common in patients with COVID-19 and could predict the outcome. Patients with non-alcoholic fatty liver disease and liver fibrosis are at higher risk of progressing to severe COVID-19.

**Key words:** SARS-CoV-2 infection, COVID-19 disease, liver function tests (LFTs), obesity, liver steatosis, liver fibrosis.
## Introduction

Coronaviruses are enveloped viruses that display the largest genome of all RNA viruses known to cause respiratory infections in humans (1). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the novel coronavirus disease that first occurred in 2019 (COVID-19) and became a serious global public health problem, declared by World Health Organization (WHO) as a global public health emergency. It can cause respiratory, intestinal, hepatic, and neuronal disease and may lead to acute respiratory distress syndrome, multiple organ failure, and death in severe cases (2).

As this infectious disease continues to spread, more information is needed to better identify the patients who require hospitalization or those who are likely develop severe COVID-19 disease. The identification of risk factors associated with outcomes should allow for patient stratification in view of personalized clinical care, in order to improve the outcome.

Liver impairment has been commonly reported in COVID-19 patients (3). Abnormal liver function tests (LFTs) are present on admission, prior to initiating treatment with hepatotoxic drugs that may induce liver injury (4). However, the association between LFTs and different degrees of COVID-19 severity of is still poorly described.

The features of metabolic syndrome, including obesity, diabetes, and hypertension, are associated with a severe COVID-19 course (5). Non-alcoholic fatty liver disease (NAFLD) is associated with these metabolic comorbidities. The association and evolution of SARS-CoV-2 infection in patients with a preexisting liver disease like NAFLD are still incompletely understood.

The current study aimed to investigate liver injury, clinical features, and risk factors in patients with associated mild, moderate, and severe COVID-19.

## Patients and Methods

### Study design and participants

We retrospectively included all consecutive patients hospitalized for SARS-CoV-2 infection between February, 22 and May, 16, 2020 at the emergency rooms (ERs) of two French tertiary hospitals (Nouvel Hôpital Civil and Hôpital de Hautepierre - University Hospital of
Strasbourg). The study protocol was approved by the Ethics Committee of Strasbourg
University Hospital (CE-2020-138-26/08/2020).

Data Collection

The data collected for the hospitalized patients suffering from SARS-CoV-2 infection included their epidemiological and medical history, clinical signs, and biological and imaging data only at admission.

Three groups of patients were defined according to the hospitalization: (1) **mild disease**: patients hospitalized at the emergency room (ER) and discharged from the hospital without any indication of admission to a conventional hospitalization unit (CHU) or intensive care unit (ICU); (2) **moderate disease**: patients admitted to the ER and hospitalized thereafter in a CHU without signs of severe disease that would need intensive care support; (3) **severe disease**: patients needing intensive care support on admission or during the hospitalization in a CHU, patients hospitalized in the ICU, and patients who died during hospitalization in a CHU.

Liver tests were considered abnormal if the liver enzyme values, i.e., aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), and total bilirubin (TBIL), were elevated according to our laboratory upper limit of normal (ULN). Different patterns were defined: hepatocellular, cholestatic, and mixed patterns. The hepatocellular pattern was considered if AST or ALT were elevated higher than ULN; the cholestatic pattern if GGT or ALP were elevated up to ULN; the mixed pattern was a combination of the first two ones.

The exclusion criteria were described in the figure 1, as follows: patients with multiple hospitalizations for COVID-19, hospitalization for a pathology that may disturb LFTs (liver metastasis, cholangitis, cholecystitis, pancreatitis), patients transferred to other hospitals in Strasbourg or elsewhere, lost to follow-up or with missing clinical and biological data, patients with underlying chronic liver disease (viral, alcohol, autoimmune). Patients with diagnosed NAFLD were included in the study.
Moreover, NAFLD was **diagnosed** using the hepatic steatosis index (HSI = 8 × [ALT/AST] + body mass index [BMI] [+ 2 if type 2 diabetes yes, + 2 if female]) >36 points (6) and the severity related to the stage fibrosis with NAFLD fibrosis score (NFS) (7). This HSI has been validated in COVID-19 patients to evaluate NAFLD (8-10). The fibrosis was also evaluated using fibrosis-4 index (FIB-4): low (<1.30), intermediate (1.30-2.67), and high (>2.67) (11). To evaluate the severity of the metabolic liver disease, we analyzed the different FIB-4 scores in patients diagnosed with NAFLD according to the HSI score.

Quantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) tests for SARS-CoV-2 nucleic acid were performed on admission with nasopharyngeal swabs. The primer and probe sequences targeted two regions on the RdRp gene and were specific to SARS-CoV-2. The assay sensitivity was around 10 copies/reaction (in-house method, *Institut Pasteur*, Paris, France) (12).

Chest computer tomography (CT) imaging was performed on admission for each patient. The images were classified based on the percentage of the whole lung parenchyma affected by COVID-19 in the following six groups: normal (no lesion), minimal (0%–10%), moderate (11%–25%), important (26%–50%), severe (51%–75%), and critical (>75%) (13). To simplify clinical data analysis, the patients were divided into three subgroups: ≤25%, 26%–50%, and >50%, according to their lesion extension.

## Statistics

The variables collected were expressed as the numbers and frequencies for qualitative variables (ordinal and categorical) and minimum, maximum, median, mean, and standard deviation for quantitative variables (discrete and continuous). These data were summarized in the descriptive phase.

For the bivariate analysis between groups, Fisher's exact test or Chi-squared tests were applied to compare qualitative variables, and Wilcoxon or Student t-tests were employed to compare quantitative variables.

To analyze the primary outcome, logistic regression was carried out by selecting variables from the literature, performing bivariate analysis, and then further selecting using Akaike information criterion (AIC).
Missing data were imputed by multiple imputations if they did not concern more than 30% of the data available; otherwise, the variable was excluded from the model.

The alpha risk was set at 5%, and all analyses were performed using R software, Version 4.0.

A multivariate analysis was performed after adjusting for confounding factors that might disturb the LFTs on admission, such as hypoxemia (capillary oxygen saturation [SaO₂] <93%, related to severe pneumopathy) (14) and hepatotoxic drugs (acetaminophen; antibiotics) administered prior to hospitalization. Hypotension, defined by systolic blood pressure <90mmHg, was not considered because of the low number of cases concerned (<10).

## Results

Between February, 22 and May, 15, 2020, a total of 1,381 patients with SARS-CoV-2 infection were admitted to the ERs. After excluding 662 patients according to our exclusion criteria, 719 were included in the subsequent analyses, as presented in the study flowchart (Figure 1).

According to COVID-19 severity, 101(14%) patients displayed mild disease, 393 (54.7%) presented with moderate disease, and 225 (31.3%) developed severe disease. The mean hospitalization length was 11.7 ± 12.8 days.

The mean age was 64.7 (19–97) years old, and 387 (58.8%) patients were men. Comorbidities were present in 513 (71.3%) patients, and the most common were BMI>25Kg/m² in 479 (70.6%) and BMI>30Kg/m² in 240 (33.3%), hypertension in 386 (53.7%), dyslipidemia in 226 (31.5%), diabetes in 201 (28%), chronic cardiac disease in 232 (32.3%), chronic respiratory disease in 176 (24.5%), chronic renal disease in 59 (8.2%), and cancer in 32 (4.5%).

Liver tests were performed on admission. The frequency of different abnormal LFTs, such as AST, ALT, ALP, GGT, TBIL, albumin, and prothrombin time (PT), was 57%, 35.9%, 11.4%, 56.5%, 5.8%, 18.4%, and 9.1%, respectively, while the mean values for these parameters were 53.8 (±85.1) u/L, 46.2 (±67.6) u/L, 74.2 (±39.1) u/L, 84.4 (±104.4) u/L, 11.3 (±14.5) µmol/L, 38.2 (±5.5) g/dL, and 87.8% (±14.7), respectively.

The LFTs exhibited a hepatocellular pattern in 60.9%, cholestatic pattern in 63.1%, and mixed-type pattern in 46.8% of patients.

## Comparison between mild and moderate COVID-19 on admission
Table 1 summarizes the clinical and biological characteristics on admission for patients with mild and moderate disease.

LFT laboratory data showed higher elevations of AST (p<0.001), ALT (p=0.05), and GGT (p=0.004) in addition to lower albumin (p<0.001) and PT (p=0.042) in patients with moderate versus mild COVID-19 (Table 1). AST levels were higher in patients hospitalized in a CHU compared to those in ER: 1-2xULN (p=0.001) and >2xULN (p=0.001).

Patients with moderate COVID-19 exhibited elevated GGT levels compared to mild disease, 53.6% vs 16.7% (p=0.016) respectively and more disturbed hepatocellular (p=0.002), cholestatic (p=0.004) and mixed-type (0.004) patterns. These patients had also higher NFS (p<0.001) and more severe liver fibrosis than patients with mild COVID-19: 71.7% had an intermediate/high FIB-4 fibrosis score compared to 41.6% with mild disease (p<0.001). Patients with moderate disease had more NAFLD with an intermediate/high FIB-4 score compared to patients with mild disease (p<0.001) (Table 1).

As can be seen in Table 2, after adjusting for confounding factors that could disturb LFTs, such as hypoxemia and hepatotoxic drugs, patients with AST 2xULN and hepatocellular pattern 2xULN had a 5.5-fold higher risk of being hospitalized (OR 5.599; 95% CI: 1.29-24.25, p=0.021 and OR 5.499; 95%CI: 1.27-23.86; p=0.023, respectively). Patients with metabolic syndrome (the association of 3 metabolic risk factors (MRF)) had more than 2.5-fold higher risk of hospitalization (OR 2.56; 95%CI: 1.292-5.09, p=0.007).

Moderate disease was associated with an intermediate (OR 2.405; 95%CI: 1.39-4.15; p=0.002) and high (OR 5.415; 95%CI: 2.69-10.89; p< 0.001) FIB-4 score. Similarly, patients with an elevated NFS had a higher risk of hospitalization (OR 1.754 95%CI: 1.27-2.43; p<0.001).

In multivariate analyses, patients with metabolic syndrome had almost a 2.4-fold risk of being hospitalized (OR 2.37; 95% CI: 1.18-4.75, p=0.015).

## Comparison between mild/moderate and severe COVID-19

We compared the clinical and laboratory tests and radiological findings in patients with severe versus those with moderate and mild COVID-19 disease (Table 3).
The laboratory liver tests showed more elevated AST (p<0.001), ALT (p=0.002), and TBIL levels (p=0.01), in addition to lower albumin (p<0.001) and PT (p<0.001) in severe disease patients (Table 3).

Concerning abnormal LFT patterns, severe COVID-19 was associated with all types of patterns: hepatocellular (p<0.001), cholestatic (p=0.028), and mixed (p<0.001).

In severe disease patients, 89.3% had intermediate and high FIB-4 scores compared to 65.5% of patients with moderate and mild disease (p<0.001). More patients with severe COVID-19 had NAFLD associated with a high FIB-4: 72.8% vs. 57.9%, respectively (p<0.001) (Table 3).

After adjusting for confounding factors, as described, abnormal LFTs at admission such as AST>2x ULN (OR 3.187; 1.99–5.08; p< 0.001), hepatocellular pattern >2x ULN (2.265; 95% CI 1.48–3.45; p< 0.001), intermediate and high FIB-4 score (OR 2.006; 95%CI 1.09–3.67; p=0.024 and OR 4.756; 95%CI: 2.68–8.43; p<0.001 respectively) or metabolic syndrome (OR 2.45, 95%CI1.07-5.61, p=0.03) were associated with a worse evolution of the disease (Table 4).

In multivariate analyses, patients with high FIB-4 index had 3-fold risk to develop a severe disease (OR 3.11; 95%CI 1.4-6.91, p=0.005).

## Discussion

Coronavirus disease 2019, the pandemic caused by SARS-CoV-2, has been associated with substantial morbidity and mortality (15). More information concerning SARS-CoV-2 infection is needed to rapidly identify patients likely to develop severe disease, requiring adjusted care. This is the first monocentric study that describes the outcome of a large cohort of COVID-19 patients based on clinical, laboratory, and radiological data collected at admission, enabling us to rapidly identify factors associated with outcomes that predict mild, moderate, or severe disease at the time of hospital admission the hospital.

Recently published studies have shown that patients admitted for COVID-19 often present with abnormal liver test, the incidence of liver injury ranging from 14% to 78% (1, 6, 16). Moreover, preexisting liver disease has been shown to be associated with worsening infection prognosis (17). However, the mechanistic events remain to be determined, and the
causes of LFTs may be multifactorial, including causes unrelated to COVID-19. In contrast to previous studies, we analyzed only the laboratory liver tests at baseline in order to avoid the anomalies induced by different drugs administrated during the hospitalization.

In this present cohort, on admission, abnormal LFTs were present in 68.9% of patients, being more elevated than those previously reported in China at 14.9%, yet similar to those estimated in the United States at between 40%–67.5%. This controversy could be explained by the higher proportion of obese and diabetic patients in occidental countries (18).

Hepatocellular parameters, such as the AST and ALT, were elevated in 57% and 35.9% of patients, respectively, being beyond 2xULN in 17.4% and 4%, respectively. Patients with severe disease had a higher AST compared with moderate and mild disease patients (p<0.001). The ALT was also more elevated in severe COVID-19 patients when compared with the other patient groups (p=0.02). After adjusting for confounding factors, elevated transaminases, such as AST >2xULN, and hepatocellular pattern type >2xULN were associated with a 3-fold (p<0.001) and almost 3.5-fold (p<0.001) higher risk, respectively, of a poorer clinical outcome.

Recent studies have shown that SARS-CoV-2 binds angiotensin converting enzyme 2 (ACE2) receptors localized both in the liver and bile duct cells. The expression is higher in the cholangiocytes that may be associated with cholestatic abnormalities (elevated ALP and GGT) rather than hepatocellular abnormalities (AST and ALT) (1, 20).

In our study, the ALP, GGT, and TBIL were elevated in 11.4%, 56.5%, and 5.8% of patients, respectively. Only an abnormal GGT was associated with moderate disease (GGT ULN, OR 0.108; 95%CI: 0.013–0.89, p=0.039). Interestingly, ALP >1–2xULN was more common in moderate and mild disease (91.5%) than in severe disease (83.9%) (p=0.045). Our data confirm the hypothesis of Hundt et al. reporting that the mechanism of liver damage may be not caused by direct cytopathic effects of the novel coronavirus (19).

Obesity was associated with COVID-19 disease severity (21, 22). Indeed, in our study, 72.5% of patients that required hospitalization were overweight, with 34.6% being obese. The proportion of overweight patients increased with disease severity, as follows: 58.4% in mild, 71.8% in moderate, and 73.3% in severe COVID-19. Moreover, the hospitalized patients had a higher BMI compared to those discharged from an ER: 28.9 vs. 26.9Kg/m² (p=0.001).
A strong association between obesity and the severity of COVID-19 was previously reported, even in the absence of other comorbidities, yet the underlying mechanism is unclear (23).

Obesity is characterized by a chronic inflammatory state and disrupted immune response. The main cytokines responsible for chronic inflammation are tumor necrosis factor-alpha (TNF-α), interleukin (Il)-6, and IL-1 beta(β) (5). Moreover, SARS-CoV-2 may cause a hyper-inflammatory reaction through the excessive release of cytokines, a condition known as “cytokine storm,” that may be enhanced by obesity, particularly in patients with a preexisting liver disease like NAFLD (24). Adipose tissue may also be a viral reservoir and an immunological hub for the inflammatory response (25). Individuals with severe obesity, diabetes, and hypertension are more likely to be infected and are at a higher risk of complications and death due to COVID-19 (2).

Indeed, in our study, metabolic factors were associated with the severity of COVID-19. Hypertension (p=0.002) and dyslipidemia (p=0.001) were associated with moderate disease, and diabetes was associated with a severe course of the disease (p=0.032). Patients with metabolic syndrome had a 2.5-fold higher risk to develop a moderate (p=0.007) or a severe disease (p=0.033).

Abnormal GGT is commonly observed in NAFLD and is a sensitive marker of steatosis (11). In our study, the ALP was elevated only in 11.4% of patients, and the mean values were not significantly higher in mild disease compared to moderate (p=0.938) and severe forms (p=0.178). Moreover, 56.5% of patients had abnormal GGT levels, and this was correlated with the severity of COVID-19 when comparing severe versus moderate (p=0.02) and mild disease forms (p=0.016).

The prognosis of liver mortality was related to liver fibrosis (26). Liver fibrosis evaluated with the FIB-4 score was associated with the severity of COVID-19 illness: 71.7% of patients with intermediate/high FIB-4 scores needed hospitalization compared to 41.6% of those with only mild disease (p<0.001), and it was higher in patients with severe SARS-CoV-2 infection than in those with moderate and mild disease (89.3% compared to 65.5%, respectively) (p<0.001). Patients who needed hospitalization had a more elevated NFS compared to those who were discharged from an ER (p=0.001).
We associated the FIB-4 scores in patients diagnosed with NAFLD (Tables 2 and 4). In the hospitalized patients, 84.2% had an intermediate/high FIB-4 compared to 67.3% of those discharged from an ER (p<0.001). In patients with a high FIB-4, 72.8% had severe disease compared to the 57.9% of those with moderate and mild disease (p<0.001). Moreover, the patients with NAFLD without fibrosis (FIB-4<1.3) were at a lower risk of hospitalization (OR 0.404; 95%CI: 0.20-0.81; p<0.011).

In the multivariate-adjusted analysis, patients with high FIB-4 scores had an almost 3-fold greater risk of hospitalization (p=0.011), and a worse course of disease (p<0.001).

In the context of the COVID-19 pandemic, along with the high prevalence of NAFLD in the general population, it is mandatory to early identify patients at risk of developing a severe disease. Applying non-invasive tests such as the FIB-4 and NFS in patients with metabolic liver disease could enable us to better and more quickly identify these patients that may have a poorer prognosis (27).

Our study had some limitations. First, the data were collected retrospectively, and there was missing information regarding the tobacco and alcohol consumption, several clinical parameters (waist and hip circumferences), and laboratory tests pertaining to metabolic syndrome, including glucose, glycated hemoglobin, cholesterol, and triglycerides levels. Second, while all patients underwent transaminase measurements at admission, only half of them underwent cholestasis enzyme assessment at admission, which could be deemed as a limitation in the statistical analysis. Third, HIS index was used in several publications to define NAFLD in COVID 19 patients (8-10). It is a simple, disponible score that may help us to easy identify high-risk patients. His limitation is related to the elevated transaminases levels that could underestimate the index.

In conclusion, obese patients with metabolic syndrome and a preexisting liver disease, including NAFLD and liver fibrosis, display a dysregulated hepatic innate immunity and pro-inflammatory state that is likely to enhance the “cytokine storm” due to SARS CoV-2 infection, leading to severe COVID-19 disease. Thus, abnormal LFTs at admission predict the outcome of COVID-19 patients.

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<caption>Figure 1. Flowchart of patients included in the study</caption>
<figure><img src="image_5.png" /><figcaption>Table 1. Characteristics of patients with mild and moderate COVID-19 on admission</figcaption></figure>
<table><thead><tr><th>Variable</th><th>Mild COVID-19 (n=101)</th><th>Moderate COVID-19 (n=393)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years, SD*)</td><td>57.7 (18.4)</td><td>65.7 (7)</td><td>&lt;0.001</td></tr><tr><td>Gender (male, %)</td><td>52 (51.4)</td><td>198 (50.4)</td><td>0.468</td></tr><tr><td>BMI (kg/m², SD)</td><td>26.9 (4.9)</td><td>28.8 (5)</td><td>0.004</td></tr><tr><td><strong>Comorbidity</strong></td><td></td><td></td><td></td></tr><tr><td>Diabetes (n, %)</td><td>21 (20.8)</td><td>105 (26.7)</td><td>0.251</td></tr><tr><td>Hypertension (n, %)</td><td>38 (37.6)</td><td>217 (55.2)</td><td>0.002</td></tr><tr><td>Dyslipidemia (n, %)</td><td>17 (16.8)</td><td>129 (32.8)</td><td>0.001</td></tr><tr><td>Chronic pulmonary disease (n, %)</td><td>19 (18.8)</td><td>94 (24)</td><td>0.291</td></tr><tr><td>Chronic cardiac disease (n, %)</td><td>19 (18.8)</td><td>106 (27)</td><td>0.097</td></tr><tr><td>Chronic kidney disease (n, %)</td><td>4 (4)</td><td>26 (6.6)</td><td>0.482</td></tr><tr><td>Cancer (n, %)</td><td>2 (2)</td><td>20 (5.1)</td><td>0.277</td></tr><tr><td><strong>Symptoms onset</strong></td><td></td><td></td><td></td></tr><tr><td>“Flu-like” symptoms (n, %)</td><td>87 (86.1)</td><td>366 (93.1)</td><td>0.041</td></tr><tr><td>Respiratory symptoms (n, %)</td><td>87 (86.1)</td><td>351 (89.3)</td><td>0.014</td></tr><tr><td>Digestive symptoms (n, %)</td><td>40 (39.6)</td><td>208 (52.9)</td><td>0.019</td></tr><tr><td>Neurological symptoms (n, %)</td><td>45 (44.6)</td><td>184 (46.8)</td><td>0.738</td></tr><tr><td><strong>Clinical features</strong></td><td></td><td></td><td></td></tr><tr><td>Oxygen saturation SaO2 (%, SD)</td><td>96 (3.9)</td><td>92.1 (5.9)</td><td>&lt;0.001</td></tr><tr><td>Debit of oxygen at admission (L/mn, sd)</td><td>0.4 (1.8)</td><td>2.3 (7.4)</td><td>&lt;0.001</td></tr><tr><td>Systolic arterial pressure (mmHg, SD)</td><td>132.4 (17.9)</td><td>133.1 (25.8)</td><td>0.683</td></tr><tr><td><strong>Laboratory tests</strong></td><td></td><td></td><td></td></tr><tr><td>AST U/L (reference range 11-34)</td><td>30.4 (20.4)</td><td>45.5 (36.2)</td><td>&lt;0.001</td></tr><tr><td>ALT U/L (reference range 8-41)</td><td>35.7 (27.5)</td><td>42.3 (34)</td><td>0.05</td></tr><tr><td>ALP U/L (reference range 41-117)</td><td>66.1 (13.2)</td><td>76.1 (40.6)</td><td>0.938</td></tr><tr><td>GGT U/L (reference range 6-40)</td><td>30.2 (23.7)</td><td>87.6 (119.2)</td><td>0.004</td></tr><tr><td>Bilirubin µmol/L, (reference range 1.7-21.0)</td><td>9.1 (4.9)</td><td>11.3 (18.3)</td><td>0.279</td></tr><tr><td>Albumin g/L, (reference range 35-50)</td><td>43.1 (3.9)</td><td>39.2 (5)</td><td>&lt;0.001</td></tr><tr><td>Abnormality type (n, %)</td><td></td><td></td><td></td></tr><tr><td>Hepatocellular &gt; ULN</td><td>39 (38.6)</td><td>221 (56.2)</td><td>0.002</td></tr><tr><td>Cholestatic &gt; ULN</td><td>2 (16.7)</td><td>112 (61.2)</td><td>0.004</td></tr><tr><td>Mixed &gt; ULN</td><td>0 (0)</td><td>72 (39.3)</td><td>0.004</td></tr><tr><td>PT (normal range &gt;70%) (%, SD)</td><td>92.6 (8)</td><td>87.9 (15.7)</td><td>0.042</td></tr><tr><td>CRP mg/L (reference range &lt; 4)</td><td>36.1 (54.1)</td><td>87.9 (70.5)</td><td>&lt;0.001</td></tr><tr><td>Leucocytes *10<sup>9</sup>/L (reference range 4.5-6.5)</td><td>6.2 (2.6)</td><td>7(3.4)</td><td>0.02</td></tr><tr><td>Neutrophil count *10<sup>9</sup>/L (reference range 1.3-7.7)</td><td>4.2 (2.2)</td><td>5.3 (3)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L (reference range 1.0-4.0)</td><td>1.5 (0.8)</td><td>1.2 (1.3)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L ≤1.0 (n, %)</td><td>31 (30.7)</td><td>200 (50.9)</td><td>0.001</td></tr><tr><td>Fibrinogen g/L (reference range: 2-4)</td><td>5(1.4)</td><td>5.9 (1.5)</td><td>0.016</td></tr></tbody></table>
<table><tr><td>Platelets *10<sup>9</sup>/L<br>(reference range: 150-400)</td><td>236.2 (104.6)</td><td>226.3 (99.8)</td><td>0.331</td></tr><tr><td>D-Dimers (reference range: 150-400)</td><td>765 (698)</td><td>2452.9 (4111.5)</td><td>0.05</td></tr><tr><td><strong>Metabolic and fibrosis characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>FIB-4 status (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>&lt;1.3</td><td>59 (58.4)</td><td>111 (28.3)</td><td></td></tr><tr><td>1.3-2.67</td><td>29 (28.7)</td><td>142 (36.2)</td><td></td></tr><tr><td>&gt;2.67</td><td>13 (12.9)</td><td>139 (35.5)</td><td></td></tr><tr><td>NAFLD (n, %)</td><td>62 (61.4)</td><td>249 (63.4)</td><td>0.73</td></tr><tr><td>NAFLD+ et FIB-4 (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>NAFLD + FIB-4 &lt;1.3</td><td>33 (32.7)</td><td>62 (15.8)</td><td></td></tr><tr><td>NAFLD + FIB-4 1.3-2.67</td><td>19 (18.8)</td><td>94 (23.9)</td><td></td></tr><tr><td>NAFLD + FIB-4 &gt;2.67</td><td>49 (48.5)</td><td>237 (60.3)</td><td></td></tr><tr><td>NFS (mean, SD)</td><td>-2 (1.5)</td><td>-0.7 (1.7)</td><td>&lt;0.001</td></tr><tr><td><strong>Radiologic findings</strong></td><td></td><td></td><td></td></tr><tr><td>Severity of pneumonia (CT scan)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>-absence</td><td>25 (26%)</td><td>24 (6.8%)</td><td></td></tr><tr><td>-&lt;25%</td><td>58 (60.4%)</td><td>185 (52.6%)</td><td></td></tr><tr><td>25-50%</td><td>10 (10.4%)</td><td>116 (33%)</td><td></td></tr><tr><td>&gt;50%</td><td>3 (3.1%)</td><td>27 (7.7%)</td><td></td></tr><tr><td>Delays from symptom onset to<br>hospitalization (days, SD)</td><td>7.2 (6.8)</td><td>7.6 (6.8)</td><td>0.592</td></tr><tr><td>Time of hospitalization (days, SD)</td><td>1 (0.9)</td><td>10.6 (8.5)</td><td>&lt;0.001</td></tr></table>

BMI: body mass index

SD: standard deviation

COVID-19: coronavirus disease 2019

AST: aspartate aminotransferase

ALT: alanine aminotransferase

ALP: alkaline phosphatase

ULN: upper limit of the normal

GGT: gamma-glutamyl transferase

LFTs: liver function tests

PT: prothrombin time

FIB-4: fibrosis-4

CRP: Creatine-reactive protein

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2

CT: computed tomography

HSI: hepatic steatosis index

NAFLD: non-alcoholic fatty liver disease

NFS: NAFLD fibrosis score

SaO2: capillary oxygen saturation

TBIL: total bilirubin
<table><caption>Table 2. Factors associated with moderate COVID-19, after adjusting for confounding factors</caption><thead><tr><td></td><td>OR</td><td>95%CI</td><td>p-value</td></tr></thead><tbody><tr><td>MS (MRF 1+2+3)</td><td>2.85</td><td>1.49-5.45</td><td>0.002</td></tr><tr><td>MS (MRF 1+2+4)</td><td>2.56</td><td>1.29-5.09</td><td>0.007</td></tr><tr><td>MS (MFR 1+3+4)</td><td>3.09</td><td>1.29-6.89</td><td>0.006</td></tr><tr><td>NFS score</td><td>1.754</td><td>1.27-2.43</td><td>&lt;0.001</td></tr><tr><td>FIB-4 (1.3-2.67)</td><td>2.405</td><td>1.39-4.15</td><td>0.002</td></tr><tr><td>FIB-4 &gt;2.67</td><td>5.415</td><td>2.69-10.89</td><td>&lt;0.001</td></tr><tr><td>NAFLD+ and FIB-4&lt;1.30</td><td>0.404</td><td>0.20-0.81</td><td>0.011</td></tr><tr><td>ASTULN</td><td>0.395</td><td>0.23-0.66</td><td>&lt;0.001</td></tr><tr><td>AST&gt;2 ULN</td><td>5.599</td><td>1.29-24.25</td><td>0.021</td></tr><tr><td>Hepatocellular type &gt;2 ULN</td><td>5.499</td><td>1.27-23.86</td><td>0.023</td></tr><tr><td>GGT ULN</td><td>0.108</td><td>0.01-0.89</td><td>0.039</td></tr><tr><td>Cholestatic type &gt;ULN</td><td>11.915</td><td>1.45-98.27</td><td>0.021</td></tr><tr><td>Albumin</td><td>0.805</td><td>0.70-0.92</td><td>0.002</td></tr><tr><td>Neutrophil count</td><td>1.13</td><td>1.02-1.25</td><td>0.023</td></tr><tr><td>Lymphocytes&lt;1000/µL</td><td>2.196</td><td>1.33-3.64</td><td>0.002</td></tr><tr><td>Prothrombin time</td><td>0.963</td><td>0.94-0.99</td><td>0.01</td></tr><tr><td>CRP</td><td>1.017</td><td>1.01-1.02</td><td>&lt;0.001</td></tr><tr><td>CRP &lt;100 mg/L</td><td>0.29</td><td>0.14-0.62</td><td>&lt;0.001</td></tr></tbody></table>

OR: odd ratio; CI: confidence interval; MS: metabolic syndrome, MRF: metabolic risk factors: 1-obesity; 2-diabetes, 3-dyslipidemia, 4-hypertension.

Adjustment for confounding factors that could disturb LFTs, as follows: hypoxemia (SaO2 < 93% related to severe pneumopathy) (16); hepatotoxic drugs (acetaminophen, antibiotics) administrated before hospitalization. Hypotension defined by systolic blood pressure < 90mmHg was not considered because of low case number <10
<table><caption>Table 3. Characteristics of patients with mild and moderate versus severe COVID-19 on admission.</caption><thead><tr><th>Variable</th><th>Mild and Moderate COVID-19 (n=494)</th><th>Severe COVID-19 (n=225)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years, SD*)</td><td>63.1 (17.5)</td><td>68.3 (16.9)</td><td>&lt;0.001</td></tr><tr><td>Gender (male, %)</td><td>251 (50.8)</td><td>138 (61.3)</td><td>0.94</td></tr><tr><td>BMI (Kg/m²) (mean, SD)</td><td>28.4 (5.9)</td><td>28.9 (5.9)</td><td>0.255</td></tr><tr><td><strong>Comorbidity</strong></td><td></td><td></td><td></td></tr><tr><td>Diabetes (n, %)</td><td>126 (25.5)</td><td>75 (33.3)</td><td>0.032</td></tr><tr><td>Hypertension (n, %)</td><td>255 (51.6)</td><td>131 (58.2)</td><td>0.107</td></tr><tr><td>Dyslipidemia (n, %)</td><td>146 (29.6)</td><td>80 (35.7)</td><td>0.118</td></tr><tr><td>Chronic pulmonary disease (n, %)</td><td>113 (23)</td><td>63 (28)</td><td>0.161</td></tr><tr><td>Chronic cardiac disease (n, %)</td><td>125 (25.3)</td><td>72 (32)</td><td>0.071</td></tr><tr><td>Chronic kidney disease (n, %)</td><td>30 (6.1)</td><td>29 (12.9)</td><td>0.003</td></tr><tr><td>Cancer (n, %)</td><td>22 (4.5)</td><td>10 (4.5)</td><td>1</td></tr><tr><td><strong>Symptoms onset</strong></td><td></td><td></td><td></td></tr><tr><td>“Flu-like” symptoms (n, %)</td><td>453 (91.7)</td><td>210 (93.3)</td><td>0.549</td></tr><tr><td>Respiratory symptoms (n, %)</td><td>438 (88.7)</td><td>208 (92.4)</td><td>0.435</td></tr><tr><td>Digestive symptoms (n, %)</td><td>248 (50.2)</td><td>96 (42.7)</td><td>0.064</td></tr><tr><td>Neurological symptoms (n, %)</td><td>229 (46.4)</td><td>106 (47.1)</td><td>0.872</td></tr><tr><td><strong>Clinical features</strong></td><td></td><td></td><td></td></tr><tr><td>Oxygen saturation SaO2 (%, SD)</td><td>92.9 (5.7)</td><td>85.3 (9.1)</td><td>&lt;0.001</td></tr><tr><td>Debit of oxygen at admission (l/mn, sd)</td><td>1.9 (6.7)</td><td>6.5 (7.7)</td><td>&lt;0.001</td></tr><tr><td>Systolic arterial pressure, mmHg</td><td>132.5 (20.8)</td><td>131.5 (20.9)</td><td>0.375</td></tr><tr><td><strong>Laboratory tests</strong></td><td></td><td></td><td></td></tr><tr><td>AST U/L (reference range, 11-34)</td><td>48 (87.8)</td><td>73.1 (64.6)</td><td>&lt;0.001</td></tr><tr><td>ALT U/L (reference range, 8-41)</td><td>40.9 (32.8)</td><td>57.9 (110)</td><td>0.002</td></tr><tr><td>ALP U/L (reference range, 41-117)</td><td>75.2 (39)</td><td>72.3 (39.4)</td><td>0.178</td></tr><tr><td>GGT U/L (reference range, 6-40)</td><td>84.2 (116.5)</td><td>84.7 (79.6)</td><td>0.087</td></tr><tr><td>Total Bilirubin µmol/l (reference range, 1.7-21.0)</td><td>11.1 (17.5)</td><td>11.6 (6.9)</td><td>0.01</td></tr><tr><td>Conjugated Bilirubin µmol/l (reference range&lt; 4.3)</td><td>3.7 (2.6)</td><td>4.2 (2.4)</td><td>0.055</td></tr><tr><td>Albumin g/, (reference range, 35-50)</td><td>39.7 (5)</td><td>36.4 (5.4)</td><td>&lt;0.001</td></tr><tr><td>PT (normal range &gt;70%) (%, SD)</td><td>88.9 (14.6)</td><td>85.6 (14.7)</td><td>&lt;0.001</td></tr><tr><td><strong>Abnormality type (n, %)</strong></td><td></td><td></td><td></td></tr><tr><td>Hepatocellular &gt;ULN</td><td>260 (52.6)</td><td>178 (79.1)</td><td>&lt;0.001</td></tr><tr><td>Cholestatic &gt;ULN</td><td>114 (58.5)</td><td>81 (71.1)</td><td>0.028</td></tr><tr><td>Mixed type &gt;ULN</td><td>72 (36.9)</td><td>72 (63.7)</td><td>&lt;0.001</td></tr><tr><td>CRP mg/L (reference range&lt; 4)</td><td>77.7 (70.6)</td><td>148.7 (92.6)</td><td>&lt;0.001</td></tr><tr><td>Leucocytes *10<sup>9</sup>/l</td><td>6.9 (3.3)</td><td>8.6 (4.4)</td><td>&lt;0.001</td></tr></tbody></table>
<table><tbody><tr><td>(reference range 4.5-6.5)</td><td></td><td></td><td></td></tr><tr><td>Neutrophil count *10<sup>9</sup>/l<br>(reference range 1.3-7.7)</td><td>5.1 (2.9)</td><td>6.9 (3.9)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/l<br>(reference range: 1.0-4.0)</td><td>1.2 (1.2)</td><td>1 (1.5)</td><td>&lt;0.001</td></tr><tr><td>Lymphocytes *10<sup>9</sup>/L<br>≤ 1.0 (n, %)</td><td>231 (46.8)</td><td>137 (60.9)</td><td>0.001</td></tr><tr><td>Fibrinogen g/l (reference range 2-4)</td><td>5.8 (1.5)</td><td>6.8 (1.4)</td><td>&lt;0.001</td></tr><tr><td>Platelets *10<sup>9</sup>/L<br>(reference range 150-400)</td><td>228.3 (99.8)</td><td>210.6 (5.4)</td><td>0.028</td></tr><tr><td>D-Dimers (reference range 150-400)</td><td>2302.9 (3956.9)</td><td>2849.6 (4072.5)</td><td>0.001</td></tr><tr><td><strong>Metabolic and fibrosis characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>FIB-4 score (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>≤1.3</td><td>170 (34.5)</td><td>24 (10.7)</td><td></td></tr><tr><td>1.3-2.67</td><td>171 (34.7)</td><td>67 (29.9)</td><td></td></tr><tr><td>&gt;2.67</td><td>152 (30.8)</td><td>133 (59.4)</td><td></td></tr><tr><td>NAFLD (n, %)</td><td>311 (63)</td><td>134 (59.8)</td><td>0.455</td></tr><tr><td>NAFLD+ et FIB-4 (n, %)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>NAFLD + FIB-4 &lt;1.3</td><td>95 (19.2)</td><td>20 (8.9)</td><td></td></tr><tr><td>NAFLD + FIB-4 1.3-2.67</td><td>113 (22.9)</td><td>41 (18.3)</td><td></td></tr><tr><td>NAFLD + FIB-4 &gt;2.67</td><td>286 (57.9)</td><td>163 (72.8)</td><td></td></tr><tr><td>NAFLD fibrosis score (mean, SD)</td><td>-0.8 (1.8)</td><td>-0.6 (1.6)</td><td>0.435</td></tr><tr><td><strong>Radiologic findings</strong></td><td></td><td></td><td></td></tr><tr><td>Severity of pneumonia (CT scan)</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>-absence (n, %)</td><td>49 (10.9%)</td><td>10 (5%)</td><td></td></tr><tr><td>-&lt;25%</td><td>243 (54.2%)</td><td>53 (26.6%)</td><td></td></tr><tr><td>25-50%</td><td>126 (28.1%)</td><td>75 (37.7%)</td><td></td></tr><tr><td>&gt;50%</td><td>30 (6.7%)</td><td>61 (30.7%)</td><td></td></tr><tr><td>Delays from symptoms onset to<br>hospitalization (days, SD)</td><td>6.6 (6.2)</td><td>7.2 (5.3)</td><td>0.062</td></tr><tr><td>Time of hospitalization</td><td>8.7 (8.5)</td><td>19.1 (12.8)</td><td>&lt;0.001</td></tr></tbody></table>

SD, standard deviation

mean values

BMI: body mass index

SD: standard deviation

COVID-19: coronavirus disease 2019

AST: aspartate aminotransferase

ALT: alanine aminotransferase

ALP: alkaline phosphatase

ULN: upper limit of the normal

GGT: gamma-glutamyl transferase

LFTs: liver function tests

PT: prothrombin time
FIB-4: fibrosis-4

CRP: Creatine-reactive protein

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2

CT: computed tomography

HSI: hepatic steatosis index

NAFLD: non-alcoholic fatty liver disease

NFS: NAFLD fibrosis score

SaO2: capillary oxygen saturation

TBIL: total bilirubin

<table><caption>Table 4. Factors associated with moderate severe COVID-19 after adjusting for confounding factors.</caption><thead><tr><td></td><td>OR</td><td>95%CI</td><td>p-value</td></tr></thead><tbody><tr><td>MS (MRF 1+2+3+4)</td><td>2.45</td><td>1.07-5.61</td><td>0.033</td></tr><tr><td>NFS</td><td>1.181</td><td>1.01-1.37</td><td>0.033</td></tr><tr><td>FIB-4 (1.3-2.67)</td><td>2.006</td><td>1.09-3.67</td><td>0.024</td></tr><tr><td>FIB-4 > 2.67</td><td>4.756</td><td>2.68-8.43</td><td>&lt;0.001</td></tr><tr><td>AST ULN</td><td>0.406</td><td>0.27-0.61</td><td>&lt;0.001</td></tr><tr><td>AST >2 ULN</td><td>3.187</td><td>1.99-5.08</td><td>&lt;0.001</td></tr><tr><td>ALT ULN</td><td>0.716</td><td>0.48-1.05</td><td>0.09</td></tr><tr><td>Hepatocellular type>ULN</td><td>2.265</td><td>1.48-3.45</td><td>&lt;0.001</td></tr><tr><td>Hepatocellular type >2 ULN</td><td>3.404</td><td>2.12-5.47</td><td>&lt;0.001</td></tr><tr><td>GGT>1-3 ULN</td><td>1.676</td><td>0.97-2.88</td><td>0.062</td></tr><tr><td>Albumin ULN</td><td>0.422</td><td>0.21-0.82</td><td>0.01</td></tr><tr><td>Albumin</td><td>0.871</td><td>0.82-0.92</td><td>&lt;0.001</td></tr><tr><td>Leucocytes count</td><td>1.104</td><td>1.05-1.16</td><td>&lt;0.001</td></tr><tr><td>Neutrophil count</td><td>1.136</td><td>1.07-1.21</td><td>&lt;0.001</td></tr><tr><td>CRP >200 mg/L</td><td>2.785</td><td>1.66-4.66</td><td>&lt;0.001</td></tr><tr><td>CRP <100 mg/L</td><td>0.441</td><td>0.29-0.65</td><td>&lt;0.001</td></tr><tr><td>Fibrinogen</td><td>1.317</td><td>1.05-1.64</td><td>0.015</td></tr><tr><td>Lupus-anticoagulant</td><td>4.453</td><td>1.49-13.23</td><td>0.007</td></tr></tbody></table>

OR: odd ratio; CI: confidence interval; MS: metabolic syndrome, MRF: metabolic risk factors: 1-obesity; 2-diabetes, 3-dyslipidemia, 4-hypertension.

Adjustment for confounding factors that could disturb LFTs, as follows: hypoxemia (SaO2 < 93% related to severe pneumopathy) (16), and hepatotoxic drugs (acetaminophen, antibiotics) administrated before the hospitalization. Hypotension defined by systolic blood pressure <90mmHg was not considered because of the low number of cases <10.
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Abnormal liver tests and non-alcoholic fatty liver disease predict disease progression and
outcome of patients with Covid-19

Simona TRIPON¹, Pascal BILBAULT², Thibaut FABACHER³, Nicolas LEFEBVRE⁴, Sylvain
LESCUYER⁵, Emmanuel ANDRES⁶, Elise SCHMITT⁷, Sabrina GARNIER-KEPKA⁸, Pierrick Le
BORGNE⁹, Joris MULLER¹⁰, Hamid MERDJI¹¹, Frédéric CHAFFRAIX¹², Didier MUTTER¹³, Thomas
F BAUMERT¹⁴, Ferhat MEZIANI¹⁵, and Michel DOFFOEL¹⁶

¹Department of Hepatology and Gastroenterology, Service Expert de Lutte contre les Hépatites Virales d'Alsace (SELHVA), Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Institut des Maladies Virales et Hépatiques, Inserm U1110, Strasbourg, France.

² Head of Emergency Department, Hôpitaux Universitaires de Strasbourg, Regenerative Nanomedicine, INSERM UMR 1260 Fédération de Médecine Translationelle de Strasbourg, University of Strasbourg, France

³Department of Public Health, Hôpitaux Universitaires de Strasbourg, France

⁴Department of Infectious Diseases, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

⁵Internal Medicine Department, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

⁶Department of Internal Medicine, Medical Clinic B, Hôpitaux Universitaires de Strasbourg, University of Strasbourg, France

⁷Geriatric Department, Hôpitaux Universitaires de Strasbourg, EA-3072, University of Strasbourg, France

⁸Emergency Department, Hôpitaux Universitaires de Strasbourg, France

⁹Emergency Department, Hôpitaux Universitaires de Strasbourg, Regenerative Nanomedicine, INSERM UMR 1260 Fédération de Médecine Translationelle de Strasbourg, University of Strasbourg, France

¹⁰Department of Public Health, Hôpitaux Universitaires de Strasbourg, France

¹¹Medical ICU, Médecine Intensive- Réanimation, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, INSERM UMR 1260, Regenerative Nanomedicine (RNM), FMTS, Université de Strasbourg, France.

¹²Service Expert de Lutte contre les Hépatites Virales d'Alsace (SELHVA), Pôle Hépato-digestif, Nouvel Hôpital Civil, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France

¹³Department of General, Digestive, and Endocrine Surgery, Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, France
¹⁴Department of Hepatology and Gastroenterology, Pôle Hépato-digestif, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Institut des Maladies Virales et Hépatiques, Inserm U1110, Institut Hopitalo-Universitaire, University of Strasbourg, France

¹⁵Medical ICU, Médecine Intensive - Réanimation, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, INSERM UMR 1260, Regenerative Nanomedicine (RNM), FMTS, Université de Strasbourg, France.

¹⁶Faculté de Médecine, University of Strasbourg, Inserm U1110, Strasbourg, France,

## Acknowledgments

We acknowledge all the members of SELHVA (Service Expert de Lutte contre les Hépatites Virales d'Alsace) for their contribution to this research. In addition, we acknowledge the different medical teams: Francois DANION, Yves HANSMANN, Charlotte KAEUFFER, Yvon RUCH (Department of Infectious Disease), Murielle RONDEAU-LUTZ, Juliette JEANNEL, Cornelia KUHNERT, Esther NOEL, Philippe MERTZ, Jean-Christophe WEBER (Department of Internal Medicine, Nouvel Hôpital Civil), Abrar-Ahmad ZULFIQAR, Hanane SALEM (Department of Internal Medicine, Medical clinic B, Nouvel Hôpital Civil), Samira FAFI-KREMER (Virology Laboratory), Bernard GOICHOT (Department of Internal Medicine, Diabetes and Metabolic Disorders, Hôpital de Hautepierre), Frédéric de BLAY (Department of Pneumology), Gérald ROUL (Departement of Cardiology), Thierry MARTIN (Department of Rheumatology), Thierry HANNEDOUCHE (Department of Nephrology), the physicians in charge of patients at the Emergency Room, and all the departments at the Hôpitaux Universitaires de Strasbourg that were in charge of patients with SARS-CoV-2 infection.
Moreover, we acknowledge all health-care workers involved in the diagnosis and treatment of COVID-19 patients, as well as the general management of the hospital for the organization of care in Hôpitaux Universitaires de Strasbourg. We thank all patients involved in the study.

## Authors' contributions:

ST conceived and designed the study, collected patient data, performed data analysis and interpretation, and drafted the manuscript. MD conceived and designed the study, analysis, and interpretation of the manuscript and contributed to the critical revision of the manuscript. FC, JM contributed to data collection. TFB, DM, FM contributed to the critical

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revision of the manuscript. TF performed statistical analysis. All the others contributed to COVID-19 recruitment. All authors contributed to the manuscript revision and approved the submitted final version.

**Abbreviations:**

Alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), body mass index (BMI), coronavirus disease (COVID-19), fibrosis-4 (FIB-4), gamma-glutamyl transferase (GGT), hepatic steatosis index (HSI), liver function tests (LFTs), non-alcoholic fatty liver disease (NAFLD), NAFLD fibrosis score (NFS), capillary oxygen saturation (SaO2), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), total bilirubin (TBIL), prothrombin time (TP), upper limit of the normal (ULN).
**Abstract**

**Background and aims:** Coronavirus disease 2019 (COVID-19) is a serious public health issue that became rapidly pandemic. Liver injury and comorbidities, including metabolic syndrome, are associated with severe forms of the disease. This study sought to investigate liver injury, clinical features, and risk factors in patients with mild, moderate, and severe COVID-19.

**Methods:** We retrospectively included all consecutive patients hospitalized with laboratory-confirmed COVID-19 between February, 22 and May 15, 2020 at the emergency rooms of a French tertiary hospital. Medical history, symptoms, biological and imaging data were collected.

**Results:** Among the 1,381 hospitalizations for COVID-19, 719 patients underwent liver tests on admission and 496 (68.9%) patients displayed abnormal liver tests. Aspartate aminotransferase was most commonly abnormal in 57% of cases, followed by gamma-glutamyl transferase, alanine aminotransferase, albumin, alkaline phosphatase, and total bilirubin in 56.5%, 35.9%, 18.4%, 11.4%, and 5.8%. The presence of hepatocellular type more than 2xULN was associated with a higher risk of hospitalization and a worse course of severe disease (odd ratio [OR] 5.599; 95%CI: 1.27–23.86; p=0.021; OR 3.404; 95% CI: 2.12–5.47; p <0.001 respectively). A higher NAFLD fibrosis score was associated with a higher risk of hospitalization (OR 1.754; 95%CI: 1.27–2.43, p<0.001). In multivariate analyses, patients with high fibrosis-4 index had a 3-fold greater risk of severe disease (p <0.001).

**Conclusion:** Abnormal liver tests are common in patients with COVID-19 and could predict the outcome. Patients with non-alcoholic fatty liver disease and liver fibrosis are at higher risk of progressing to severe COVID-19.

**Key words:** SARS-CoV-2 infection, COVID-19 disease, liver function tests (LFTs), obesity, liver steatosis, liver fibrosis.
## Introduction

Coronaviruses are enveloped viruses that display the largest genome of all RNA viruses known to cause respiratory infections in humans (1). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the novel coronavirus disease that first occurred in 2019 (COVID-19) and became a serious global public health problem, declared by World Health Organization (WHO) as a global public health emergency. It can cause respiratory, intestinal, hepatic, and neuronal disease and may lead to acute respiratory distress syndrome, multiple organ failure, and death in severe cases (2).

As this infectious disease continues to spread, more information is needed to better identify the patients who require hospitalization or those who are likely develop severe COVID-19 disease. The identification of risk factors associated with outcomes should allow for patient stratification in view of personalized clinical care, in order to improve the outcome.

Liver impairment has been commonly reported in COVID-19 patients (3). Abnormal liver function tests (LFTs) are present on admission, prior to initiating treatment with hepatotoxic drugs that may induce liver injury (4). However, the association between LFTs and different degrees of COVID-19 severity of is still poorly described.

The features of metabolic syndrome, including obesity, diabetes, and hypertension, are associated with a severe COVID-19 course (5). Non-alcoholic fatty liver disease (NAFLD) is associated with these metabolic comorbidities. The association and evolution of SARS-CoV-2 infection in patients with a preexisting liver disease like NAFLD are still incompletely understood.

The current study aimed to investigate liver injury, clinical features, and risk factors in patients with associated mild, moderate, and severe COVID-19.

## Patients and Methods

### Study design and participants

We retrospectively included all consecutive patients hospitalized for SARS-CoV-2 infection between February, 22 and May, 16, 2020 at the emergency rooms (ERs) of two French tertiary hospitals (Nouvel Hôpital Civil and Hôpital de Hautepierre - University Hospital of
Strasbourg). The study protocol was approved by the Ethics Committee of Strasbourg
University Hospital (CE-2020-138-26/08/2020).

Data Collection

The data collected for the hospitalized patients suffering from SARS-CoV-2 infection included their epidemiological and medical history, clinical signs, and biological and imaging data only at admission.

Three groups of patients were defined according to the hospitalization: (1) **mild disease**: patients hospitalized at the emergency room (ER) and discharged from the hospital without any indication of admission to a conventional hospitalization unit (CHU) or intensive care unit (ICU); (2) **moderate disease**: patients admitted to the ER and hospitalized thereafter in a CHU without signs of severe disease that would need intensive care support; (3) **severe disease**: patients needing intensive care support on admission or during the hospitalization in a CHU, patients hospitalized in the ICU, and patients who died during hospitalization in a CHU.

Liver tests were considered abnormal if the liver enzyme values, i.e., aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), and total bilirubin (TBIL), were elevated according to our laboratory upper limit of normal (ULN). Different patterns were defined: hepatocellular, cholestatic, and mixed patterns. The hepatocellular pattern was considered if AST or ALT were elevated higher than ULN; the cholestatic pattern if GGT or ALP were elevated up to ULN; the mixed pattern was a combination of the first two ones.

The exclusion criteria were described in the figure 1, as follows: patients with multiple hospitalizations for COVID-19, hospitalization for a pathology that may disturb LFTs (liver metastasis, cholangitis, cholecystitis, pancreatitis), patients transferred to other hospitals in Strasbourg or elsewhere, lost to follow-up or with missing clinical and biological data, patients with underlying chronic liver disease (viral, alcohol, autoimmune). Patients with diagnosed NAFLD were included in the study.
Moreover, NAFLD was diagnosed using the hepatic steatosis index (HSI = 8 × [ALT/AST] + body mass index [BMI] [+ 2 if type 2 diabetes yes, + 2 if female]) >36 points (6) and the severity related to the stage fibrosis with NAFLD fibrosis score (NFS) (7). This HSI has been validated in COVID-19 patients to evaluate NAFLD (8-10). The fibrosis was also evaluated using fibrosis-4 index (FIB-4): low (<1.30), intermediate (1.30-2.67), and high (>2.67) (11). To evaluate the severity of the metabolic liver disease, we analyzed the different FIB-4 scores in patients diagnosed with NAFLD according to the HSI score.

Quantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) tests for SARS-CoV-2 nucleic acid were performed on admission with nasopharyngeal swabs. The primer and probe sequences targeted two regions on the RdRp gene and were specific to SARS-CoV-2. The assay sensitivity was around 10 copies/reaction (in-house method, Institut Pasteur, Paris, France) (12).

Chest computer tomography (CT) imaging was performed on admission for each patient. The images were classified based on the percentage of the whole lung parenchyma affected by COVID-19 in the following six groups: normal (no lesion), minimal (0%–10%), moderate (11%–25%), important (26%–50%), severe (51%–75%), and critical (>75%) (13). To simplify clinical data analysis, the patients were divided into three subgroups: ≤25%, 26%–50%, and >50%, according to their lesion extension.

## Statistics

The variables collected were expressed as the numbers and frequencies for qualitative variables (ordinal and categorical) and minimum, maximum, median, mean, and standard deviation for quantitative variables (discrete and continuous). These data were summarized in the descriptive phase.

For the bivariate analysis between groups, Fisher's exact test or Chi-squared tests were applied to compare qualitative variables, and Wilcoxon or Student t-tests were employed to compare quantitative variables.

To analyze the primary outcome, logistic regression was carried out by selecting variables from the literature, performing bivariate analysis, and then further selecting using Akaike information criterion (AIC).
Missing data were imputed by multiple imputations if they did not concern more than 30% of the data available; otherwise, the variable was excluded from the model.

The alpha risk was set at 5%, and all analyses were performed using R software, Version 4.0.

A multivariate analysis was performed after adjusting for confounding factors that might disturb the LFTs on admission, such as hypoxemia (capillary oxygen saturation [SaO₂] <93%, related to severe pneumopathy) (14) and hepatotoxic drugs (acetaminophen; antibiotics) administered prior to hospitalization. Hypotension, defined by systolic blood pressure <90mmHg, was not considered because of the low number of cases concerned (<10).

## Results

Between February, 22 and May, 15, 2020, a total of 1,381 patients with SARS-CoV-2 infection were admitted to the ERs. After excluding 662 patients according to our exclusion criteria, 719 were included in the subsequent analyses, as presented in the study flowchart (Figure 1).

According to COVID-19 severity, 101(14%) patients displayed mild disease, 393 (54.7%) presented with moderate disease, and 225 (31.3%) developed severe disease. The mean hospitalization length was 11.7 ± 12.8 days.

The mean age was 64.7 (19–97) years old, and 387 (58.8%) patients were men.

Comorbidities were present in 513 (71.3%) patients, and the most common were BMI>25Kg/m² in 479 (70.6%) and BMI>30Kg/m² in 240 (33.3%), hypertension in 386 (53.7%), dyslipidemia in 226 (31.5%), diabetes in 201 (28%), chronic cardiac disease in 232 (32.3%), chronic respiratory disease in 176 (24.5%), chronic renal disease in 59 (8.2%), and cancer in 32 (4.5%).

Liver tests were performed on admission. The frequency of different abnormal LFTs, such as AST, ALT, ALP, GGT, TBIL, albumin, and prothrombin time (PT), was 57%, 35.9%, 11.4%, 56.5%, 5.8%, 18.4%, and 9.1%, respectively, while the mean values for these parameters were 53.8 (±85.1) u/L, 46.2 (±67.6) u/L, 74.2 (±39.1) u/L, 84.4 (±104.4) u/L, 11.3 (±14.5) µmol/L, 38.2 (±5.5) g/dL, and 87.8% (±14.7), respectively.

The LFTs exhibited a hepatocellular pattern in 60.9%, cholestatic pattern in 63.1%, and mixed-type pattern in 46.8% of patients.

## Comparison between mild and moderate COVID-19 on admission