Association Between the LZTFL1 rs11385942 Polymorphism and COVID-19 Severity in Colombian Population - Angulo-Aguado, Mariana; Corredor-Orlandelli, David; Carrillo-Martínez, Juan Camilo; Gonzalez-Cornejo, Mónica; Pineda-Mateus, Eliana; Rojas, Carolina; Triana-Fonseca, Paula; Contreras Bravo, Nora Constanza; Morel, Adrien; Parra Abaunza, Katherine; Restrepo, Carlos M.; Fonseca-Mendoza, Dora Janeth; et al. (Frontiers in Medicine, 2022)
Genetic and non-genetic factors are responsible for the high interindividual variability in the response to SARS-CoV-2. Although numerous genetic polymorphisms have been identified as risk factors for severe COVID-19, these remain understudied in Latin-American populations. This study evaluated the association of non-genetic factors and three polymorphisms: ACE rs4646994, ACE2 rs2285666 and LZTFL1 rs11385942, with COVID severity and long-term symptoms by using a case-control design. The control group was composed of asymptomatic/mild cases (n=61) recruited from a private laboratory, while the case group was composed of severe/critical patients (n=63) hospitalized in the Hospital Universitario Mayor-Méderi, both institutions located in Bogotá, Colombia. Clinical follow up and exhaustive revision of medical records allowed us to assess non-genetic factors. Genotypification of the polymorphism of interest was performed by amplicon size analysis and Sanger sequencing. In agreement with previous reports, we found a statistically significant association between age, male gender, and comorbidities, such as hypertension and type 2 diabetes mellitus (T2DM), and worst outcomes. We identified the polymorphism LZTFL1 rs11385942 as an important risk factor for hospitalization (p<0.01; OR=5.73; 95% CI=1.2-26.5, under the allelic test). Furthermore, long-term symptoms were common among the studied population and associated with disease severity. No association between the polymorphisms examined and long-term symptoms was found. Comparison of allelic frequencies with other populations revealed significant differences for the three polymorphisms investigated. Finally, we used the statistically significant genetic and non-genetic variables to develop a predictive logistic regression model, which was implemented in a Shiny web application. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC = 0.86; 95% confidence interval 0.79-0.93). These results suggest that LZTFL1 rs11385942 may be a potential biomarker for COVID-19 severity in addition to conventional non-genetic risk factors. A better understanding of the impact of these genetic risk factors may be useful to prioritize high-risk individuals and decrease the morbimortality caused by SARS-CoV2 and future pandemics.