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PLoS One ; 17(2): e0263140, 2022.
Article in English | MEDLINE | ID: covidwho-1854993


BACKGROUND: Infection by the SARS-Cov-2 virus produces in humans a disease of highly variable and unpredictable severity. The presence of frequent genetic single nucleotide polymorphisms (SNPs) in the population might lead to a greater susceptibility to infection or an exaggerated inflammatory response. SARS-CoV-2 requires the presence of the ACE2 protein to enter in the cell and ACE2 is a regulator of the renin-angiotensin system. Accordingly, we studied the associations between 8 SNPs from AGTR1, ACE2 and ACE genes and the severity of the disease produced by the SARS-Cov-2 virus. METHODS: 318 (aged 59.6±17.3 years, males 62.6%) COVID-19 patients were grouped based on the severity of symptoms: Outpatients (n = 104, 32.7%), hospitalized on the wards (n = 73, 23.0%), Intensive Care Unit (ICU) (n = 84, 26.4%) and deceased (n = 57, 17.9%). Comorbidity data (diabetes, hypertension, obesity, lung disease and cancer) were collected for adjustment. Genotype distribution of 8 selected SNPs among the severity groups was analyzed. RESULTS: Four SNPs in ACE2 were associated with the severity of disease. While rs2074192 andrs1978124showed a protector effectassuming an overdominant model of inheritance (G/A vs. GG-AA, OR = 0.32, 95%CI = 0.12-0.82; p = 0.016 and A/G vs. AA-GG, OR = 0.37, 95%CI: 0.14-0.96; p = 0.038, respectively); the SNPs rs2106809 and rs2285666were associated with an increased risk of being hospitalized and a severity course of the disease with recessive models of inheritance (C/C vs. T/C-T/T, OR = 11.41, 95% CI: 1.12-115.91; p = 0.012) and (A/A vs. GG-G/A, OR = 12.61, 95% CI: 1.26-125.87; p = 0.0081). As expected, an older age (OR = 1.47), male gender (OR = 1.98) and comorbidities (OR = 2.52) increased the risk of being admitted to ICU or death vs more benign outpatient course. Multivariable analysis demonstrated the role of the certain genotypes (ACE2) with the severity of COVID-19 (OR: 0.31, OR 0.37 for rs2074192 and rs1978124, and OR = 2.67, OR = 2.70 for rs2106809 and rs2285666, respectively). Hardy-Weinberg equilibrium in hospitalized group for I/D SNP in ACE was not showed (p<0.05), which might be due to the association with the disease. No association between COVID-19 disease and the different AGTR1 SNPs was evidenced on multivariable, nevertheless the A/A genotype for rs5183 showed an higher hospitalization risk in patients with comorbidities. CONCLUSIONS: Different genetic variants in ACE2 were associated with a severe clinical course and death groups of patients with COVID-19. ACE2 common SNPs in the population might modulate severity of COVID-19 infection independently of other known markers like gender, age and comorbidities.

Angiotensin-Converting Enzyme 2/genetics , COVID-19/pathology , Peptidyl-Dipeptidase A/genetics , Polymorphism, Single Nucleotide , Receptor, Angiotensin, Type 1/genetics , SARS-CoV-2/genetics , Severity of Illness Index , Aged , COVID-19/genetics , COVID-19/virology , Female , Genotype , Humans , Male , Middle Aged
PLoS One ; 16(6): e0253465, 2021.
Article in English | MEDLINE | ID: covidwho-1280631


INTRODUCTION: This study was aimed to identify risk factors associated with unfavorable outcomes (composite outcome variable: mortality and need for mechanical ventilation) in patients hospitalized in Galicia with COVID-19 pneumonia. METHODS: Retrospective, multicenter, observational study carried out in the 8 Galician tertiary hospitals. All Patients admitted with confirmed COVID-19 pneumonia from 1st of March to April 24th, 2020 were included. A multivariable logistic regression analysis was performed in order to identify the relationship between risk factors, therapeutic interventions and the composite outcome variable. RESULTS: A total of 1292 patients (56.1% male) were included. Two hundred and twenty-five (17.4%) died and 327 (25.3%) reached the main outcome variable. Age [odds ratio (OR) = 1.03 (95% confidence interval (CI): 1.01-1.04)], CRP quartiles 3 and 4 [OR = 2.24 (95% CI: 1.39-3.63)] and [OR = 3.04 (95% CI: 1.88-4.92)], respectively, Charlson index [OR = 1.16 (95%CI: 1.06-1.26)], SaO2 upon admission [OR = 0.93 (95% CI: 0.91-0.95)], hydroxychloroquine prescription [OR = 0.22 (95%CI: 0.12-0.37)], systemic corticosteroids prescription [OR = 1.99 (95%CI: 1.45-2.75)], and tocilizumab prescription [OR = 3.39 (95%CI: 2.15-5.36)], significantly impacted the outcome. Sensitivity analysis using different alternative logistic regression models identified consistently the ratio admissions/hospital beds as a predictor of the outcome [OR = 1.06 (95% CI: 1.02-1.11)]. CONCLUSION: These findings may help to identify patients at hospital admission with a higher risk of death and may urge healthcare authorities to implement policies aimed at reducing deaths by increasing the availability of hospital beds.

Antiviral Agents/therapeutic use , COVID-19/mortality , COVID-19/therapy , Adrenal Cortex Hormones/therapeutic use , Aged , Aged, 80 and over , COVID-19/epidemiology , Comorbidity , Female , Hospitals/statistics & numerical data , Humans , Hydroxychloroquine/therapeutic use , Male , Middle Aged , Respiration, Artificial , Retrospective Studies , Risk Factors , Spain/epidemiology , Treatment Outcome
Int J Environ Res Public Health ; 18(10)2021 05 12.
Article in English | MEDLINE | ID: covidwho-1227018


In this work we look at the past in order to analyze four key variables after one year of the COVID-19 pandemic in Galicia (NW Spain): new infected, hospital admissions, intensive care unit admissions and deceased. The analysis is presented by age group, comparing at each stage the percentage of the corresponding group with its representation in the society. The time period analyzed covers 1 March 2020 to 1 April 2021, and includes the influence of the B.1.1.7 lineage of COVID-19 which in April 2021 was behind 90% of new cases in Galicia. It is numerically shown how the pandemic affects the age groups 80+, 70+ and 60+, and therefore we give information about how the vaccination process could be scheduled and hints at why the pandemic had different effects in different territories.

COVID-19 , Pandemics , Humans , Pandemics/prevention & control , SARS-CoV-2 , Spain/epidemiology