Androgen receptor polyQ alleles and COVID-19 severity in men: A replication study.

BACKGROUND: Ample evidence indicates a sex-related difference in severity of COVID-19, with less favorable outcomes observed in men. Genetic factors have been proposed as candidates to explain this difference. The polyglutamine (polyQ) polymorphism in the androgen receptor gene has been recently described as a genetic biomarker of COVID-19 severity. OBJECTIVE: To test the association between the androgen receptor polyQ polymorphism and COVID-19 severity in a large cohort of COVID-19 male patients. MATERIALS AND METHODS: This study included 1136 male patients infected with SARS-CoV-2 as confirmed by positive PCR. Patients were retrospectively and prospectively enrolled from March to November 2020. Patients were classified according to their severity into three categories: oligosymptomatic, hospitalized and severe patients requiring ventilatory support. The number of CAG repeats (polyQ polymorphism) at the androgen receptor was obtained by PCR and patients were classified as either short (<23 repeats) or long (≥23 repeats) allele carriers. The association between polyQ alleles (short or long) and COVID-19 severity was assessed by Chi-squared (Chi2 ) and logistic regression analysis. RESULTS: The mean number of polyQ CAG repeats was 22 (±3). Patients were classified as oligosymptomatic (15.5%), hospitalized (63.2%), and severe patients (21.3%) requiring substantial respiratory support. PolyQ alleles distribution did not show significant differences between severity classes in our cohort (Chi2 test p > 0.05). Similar results were observed after adjusting by known risk factors such as age, comorbidities, and ethnicity (multivariate logistic regression analysis). DISCUSSION: Androgen sensitivity may be a critical factor in COVID-19 disease severity. However, we did not find an association between the polyQ polymorphism and the COVID-19 severity. Additional studies are needed to clarify the mechanism underlying the association between androgens and COVID-19 outcome. CONCLUSIONS: The results obtained in our study do not support the role of this polymorphism as biomarker of COVID-19 severity.

Age-dependent association of clonal hematopoiesis with COVID-19 mortality in patients over 60 years.

Clonal hematopoiesis, especially that of indeterminate potential (CHIP), has been associated with age-related diseases, such as those contributing to a more severe COVID-19. Four studies have attempted to associate CHIP with COVID-19 severity without conclusive findings. In the present work, we explore the association between CHIP and COVID-19 mortality. Genomic DNA extracted from peripheral blood of COVID-19 patients (n = 241 deceased, n = 239 survivors) was sequenced with the Myeloid Solutions™ panel of SOPHiA Genetics. The association between clonality and age and clonality and mortality was studied using logistic regression models adjusted for sex, ethnicity, and comorbidities. The association with mortality was performed with patients stratified into four groups of age according to the quartiles of the distribution: 60-74 years, 75-84 years, 85-91 years, and 92-101 years. Clonality was found in 38% of the cohort. The presence of CHIP variants, but not the number, significantly increased with age in the entire cohort of COVID-19 patients, as well as in the group of survivors (p < 0.001). When patients were stratified by age and the analysis adjusted, CHIP classified as pathogenic/likely pathogenic was significantly more represented in deceased patients compared with survivors in the group of 75-84 years (34.6% vs 13.7%, p = 0.020). We confirmed the well-established linear relationship between age and clonality in the cohort of COVID-19 patients and found a significant association between pathogenic/likely pathogenic CHIP and mortality in patients from 75 to 84 years that needs to be further validated.

Novel genes and sex differences in COVID-19 severity.

Here, we describe the results of a genome-wide study conducted in 11 939 coronavirus disease 2019 (COVID-19) positive cases with an extensive clinical information that were recruited from 34 hospitals across Spain (SCOURGE consortium). In sex-disaggregated genome-wide association studies for COVID-19 hospitalization, genome-wide significance (P < 5 × 10-8) was crossed for variants in 3p21.31 and 21q22.11 loci only among males (P = 1.3 × 10-22 and P = 8.1 × 10-12, respectively), and for variants in 9q21.32 near TLE1 only among females (P = 4.4 × 10-8). In a second phase, results were combined with an independent Spanish cohort (1598 COVID-19 cases and 1068 population controls), revealing in the overall analysis two novel risk loci in 9p13.3 and 19q13.12, with fine-mapping prioritized variants functionally associated with AQP3 (P = 2.7 × 10-8) and ARHGAP33 (P = 1.3 × 10-8), respectively. The meta-analysis of both phases with four European studies stratified by sex from the Host Genetics Initiative (HGI) confirmed the association of the 3p21.31 and 21q22.11 loci predominantly in males and replicated a recently reported variant in 11p13 (ELF5, P = 4.1 × 10-8). Six of the COVID-19 HGI discovered loci were replicated and an HGI-based genetic risk score predicted the severity strata in SCOURGE. We also found more SNP-heritability and larger heritability differences by age (<60 or ≥60 years) among males than among females. Parallel genome-wide screening of inbreeding depression in SCOURGE also showed an effect of homozygosity in COVID-19 hospitalization and severity and this effect was stronger among older males. In summary, new candidate genes for COVID-19 severity and evidence supporting genetic disparities among sexes are provided.

Presence of rare potential pathogenic variants in subjects under 65 years old with very severe or fatal COVID-19.

Rare variants affecting host defense against pathogens could be involved in COVID-19 severity and may help explain fatal outcomes in young and middle-aged patients. Our aim was to report the presence of rare genetic variants in certain genes, by using whole exome sequencing, in a selected group of COVID-19 patients under 65 years who required intubation or resulting in death (n = 44). To this end, different etiopathogenic mechanisms were explored using gene prioritization-based analysis in which genes involved in immune response, immunodeficiencies or blood coagulation were studied. We detected 44 different variants of interest, in 29 different patients (66%). Some of these variants were previously described as pathogenic and were located in genes mainly involved in immune response. A network analysis, including the 42 genes with candidate variants, showed three main components, consisting of 25 highly interconnected genes related to immune response and two additional networks composed by genes enriched in carbohydrate metabolism and in DNA metabolism and repair processes. In conclusion, we have detected candidate variants that may potentially influence COVID-19 outcome in our cohort of patients. Further studies are needed to confirm the ultimate role of the genetic variants described in the present study on COVID-19 severity.

High SARS-CoV-2 viral load is associated with a worse clinical outcome of COVID-19 disease.

COVID-19 severity and progression are determined by several host and virological factors that may influence the final outcome of SARS-CoV-2-infected patients. The objective of this work was to determine a possible association between viral load, obtained from nasopharyngeal swabs, and the severity of the infection in a cohort of 448 SARS-CoV-2-infected patients from a hospital in Madrid during the first outbreak of the pandemic in Spain. To perform this, we clinically classified patients as mild, moderate and severe COVID-19 according to a number of clinical parameters such as hospitalization requirement, need of oxygen therapy, admission to intensive care units and/or death. Also, Ct values were determined using SARS-CoV-2-specific oligonucleotides directed to ORF1ab. Here we report a statistically significant association between viral load and disease severity, a high viral load being associated with worse clinical prognosis, independently of several previously identified risk factors such as age, sex, hypertension, cardiovascular disease, diabetes, obesity and lung disease (asthma and chronic obstructive pulmonary disease). The data presented here reinforce viral load as a potential biomarker for predicting disease severity in SARS-CoV-2-infected patients. It is also an important parameter in viral evolution since it relates to the numbers and types of variant genomes present in a viral population, a potential determinant of disease progression.

IL-6-based mortality prediction model for COVID-19: Validation and update in multicenter and second wave cohorts.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a highly variable condition. Validated tools to assist in the early detection of patients at high risk of mortality can help guide medical decisions. OBJECTIVE: We sought to validate externally, as well as in patients from the second pandemic wave in Europe, our previously developed mortality prediction model for hospitalized COVID-19 patients. METHODS: Three validation cohorts were generated: 2 external with 185 and 730 patients from the first wave and 1 internal with 119 patients from the second wave. The probability of death was calculated for all subjects using our prediction model, which includes peripheral blood oxygen saturation/fraction of inspired oxygen ratio, neutrophil-to-lymphocyte ratio, lactate dehydrogenase, IL-6, and age. Discrimination and calibration were evaluated in the validation cohorts. The prediction model was updated by reestimating individual risk factor effects in the overall cohort (N = 1477). RESULTS: The mortality prediction model showed good performance in the external validation cohorts 1 and 2, and in the second wave validation cohort 3 (area under the receiver-operating characteristic curve, 0.94, 0.86, and 0.86, respectively), with excellent calibration (calibration slope, 0.86, 0.94, and 0.79; intercept, 0.05, 0.03, and 0.10, respectively). The updated model accurately predicted mortality in the overall cohort (area under the receiver-operating characteristic curve, 0.91), which included patients from both the first and second COVID-19 waves. The updated model was also useful to predict fatal outcome in patients without respiratory distress at the time of evaluation. CONCLUSIONS: This is the first COVID-19 mortality prediction model validated in patients from the first and second pandemic waves. The COR+12 online calculator is freely available to facilitate its implementation (https://utrero-rico.shinyapps.io/COR12_Score/).