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Mol Cells ; 44(9): 680-687, 2021 Sep 30.
Article in English | MEDLINE | ID: covidwho-1444539


Coronavirus disease, COVID-19 (coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has a higher case fatality rate in European countries than in others, especially East Asian ones. One potential explanation for this regional difference is the diversity of the viral infection efficiency. Here, we analyzed the allele frequencies of a nonsynonymous variant rs12329760 (V197M) in the TMPRSS2 gene, a key enzyme essential for viral infection and found a significant association between the COVID-19 case fatality rate and the V197M allele frequencies, using over 200,000 present-day and ancient genomic samples. East Asian countries have higher V197M allele frequencies than other regions, including European countries which correlates to their lower case fatality rates. Structural and energy calculation analysis of the V197M amino acid change showed that it destabilizes the TMPRSS2 protein, possibly negatively affecting its ACE2 and viral spike protein processing.

COVID-19/genetics , COVID-19/mortality , Serine Endopeptidases/genetics , COVID-19/ethnology , Gene Frequency , Humans , Models, Molecular , Mortality , Polymorphism, Single Nucleotide , Republic of Korea , Serine Endopeptidases/chemistry
J Korean Med Sci ; 36(15): e108, 2021 Apr 19.
Article in English | MEDLINE | ID: covidwho-1194583


BACKGROUND: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19. METHODS: Collaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score). The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves. RESULTS: The incidence of mortality was 4.3% in both the development and validation set. A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85-0.91) and 0.97 (95% CI, 0.84-0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator ( CONCLUSION: This clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.

COVID-19/mortality , Hospital Mortality , SARS-CoV-2 , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Logistic Models , Male , Middle Aged , Prospective Studies , Republic of Korea/epidemiology , Young Adult