Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2023 Aug 12;15(8):1724.
doi: 10.3390/v15081724.

Genetic Predictors of Comorbid Course of COVID-19 and MAFLD: A Comprehensive Analysis

Mykhailo Buchynskyi  1 , Valentyn Oksenych  2 , Iryna Kamyshna  3 , Sandor G Vari  4 , Aleksandr Kamyshnyi  1
Affiliations
Review

Genetic Predictors of Comorbid Course of COVID-19 and MAFLD: A Comprehensive Analysis

Mykhailo Buchynskyi et al. Viruses. .

Abstract

Metabolic-associated fatty liver disease (MAFLD) and its potential impact on the severity of COVID-19 have gained significant attention during the pandemic. This review aimed to explore the genetic determinants associated with MAFLD, previously recognized as non-alcoholic fatty liver disease (NAFLD), and their potential influence on COVID-19 outcomes. Various genetic polymorphisms, including PNPLA3 (rs738409), GCKR (rs780094), TM6SF2 (rs58542926), and LYPLAL1 (rs12137855), have been investigated in relation to MAFLD susceptibility and progression. Genome-wide association studies and meta-analyses have revealed associations between these genetic variants and MAFLD risk, as well as their effects on lipid metabolism, glucose regulation, and liver function. Furthermore, emerging evidence suggests a possible connection between these MAFLD-associated polymorphisms and the severity of COVID-19. Studies exploring the association between indicated genetic variants and COVID-19 outcomes have shown conflicting results. Some studies observed a potential protective effect of certain variants against severe COVID-19, while others reported no significant associations. This review highlights the importance of understanding the genetic determinants of MAFLD and its potential implications for COVID-19 outcomes. Further research is needed to elucidate the precise mechanisms linking these genetic variants to disease severity and to develop gene profiling tools for the early prediction of COVID-19 outcomes. If confirmed as determinants of disease severity, these genetic polymorphisms could aid in the identification of high-risk individuals and in improving the management of COVID-19.

Keywords: COVID-19; GCKR; LYPLAL1; MAFLD; NAFLD; PNPLA; SARS-CoV-2; TM6SF2; rs12137855; rs58542926; rs738409; rs780094.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Genetic polymorphisms contributing to the risk of NAFLD and COVID-19.
Figure 2
Figure 2
The heatmap plot illustrates the distribution of molecular traits associated with various tissue/cell types and compares them to eQTL-associated traits (genes). Each row corresponds to a unique tissue or cell type, and each column represents a distinct eQTL-associated trait (gene). The color of each grid cell indicates the median P-value of the eQTLs associated with the specific tissue and trait combination. Overview of eQTL for TM6SF2 rs58542926 (a) 47 tissues and 25 traits; PNPLA3rs738409 (b) 13 tissues and 22 traits; GCKR rs780094 (c) 44 tissues and 50 traits; GCKR rs780094 (d) 11 tissues and 5 traits.
Figure 2
Figure 2
The heatmap plot illustrates the distribution of molecular traits associated with various tissue/cell types and compares them to eQTL-associated traits (genes). Each row corresponds to a unique tissue or cell type, and each column represents a distinct eQTL-associated trait (gene). The color of each grid cell indicates the median P-value of the eQTLs associated with the specific tissue and trait combination. Overview of eQTL for TM6SF2 rs58542926 (a) 47 tissues and 25 traits; PNPLA3rs738409 (b) 13 tissues and 22 traits; GCKR rs780094 (c) 44 tissues and 50 traits; GCKR rs780094 (d) 11 tissues and 5 traits.
See this image and copyright information in PMC

References

    1. WHO Coronavirus (COVID-19) Dashboard. [(accessed on 3 June 2023)]. Available online: https://covid19.who.int/
    1. Rapp J.L., Lieberman-Cribbin W., Tuminello S., Taioli E. Male Sex, Severe Obesity, Older Age, and Chronic Kidney Disease Are Associated With COVID-19 Severity and Mortality in New York City. Chest. 2021;159:112–115. doi: 10.1016/j.chest.2020.08.2065. - DOI - PMC - PubMed
    1. Zhang J.-J., Cao Y.-Y., Tan G., Dong X., Wang B.-C., Lin J., Yan Y.-Q., Liu G.-H., Akdis M., Akdis C.A., et al. Clinical, Radiological, and Laboratory Characteristics and Risk Factors for Severity and Mortality of 289 Hospitalized COVID-19 Patients. Allergy Eur. J. Allergy Clin. Immunol. 2021;76:533–550. doi: 10.1111/all.14496. - DOI - PMC - PubMed
    1. Ebinge J.E., Achamallah N., Ji H., Clagget B.L., Sun N., Botting P., Nguyen T.T., Luong E., Ki E.H., Park E., et al. Pre-Existing Traits Associated with COVID-19 Illness Severity. PLoS ONE. 2020;15:e0236240. doi: 10.1371/journal.pone.0236240. - DOI - PMC - PubMed
    1. Williamson E.J., Walker A.J., Bhaskaran K., Bacon S., Bates C., Morton C.E., Curtis H.J., Mehrkar A., Evans D., Inglesby P., et al. Factors Associated with COVID-19-Related Death Using OpenSAFELY. Nature. 2020;584:430–436. doi: 10.1038/s41586-020-2521-4. - DOI - PMC - PubMed

Publication types