Genetic predispositions to psychiatric disorders and the risk of COVID-19.

BACKGROUND: Whether a genetic predisposition to psychiatric disorders is associated with coronavirus disease 2019 (COVID-19) is unknown. METHODS: Our analytic sample consisted of 287,123 white British participants in UK Biobank who were alive on 31 January 2020. We performed a genome-wide association study (GWAS) analysis for each psychiatric disorder (substance misuse, depression, anxiety, psychotic disorder, and stress-related disorders) in a randomly selected half of the study population ("base dataset"). For the other half ("target dataset"), the polygenic risk score (PRS) was calculated as a proxy of individuals' genetic predisposition to a given psychiatric phenotype using discovered genetic variants from the base dataset. Ascertainment of COVID-19 was based on the Public Health England dataset, inpatient hospital data, or death registers in UK Biobank. COVID-19 cases from hospitalization records or death records were considered "severe cases." The association between the PRS for psychiatric disorders and COVID-19 risk was examined using logistic regression. We also repeated PRS analyses based on publicly available GWAS summary statistics. RESULTS: A total of 143,562 participants (including 10,868 COVID-19 cases) were used for PRS analyses. A higher genetic predisposition to psychiatric disorders was associated with an increased risk of any COVID-19 and severe COVID-19. The adjusted odds ratio (OR) for any COVID-19 was 1.07 (95% confidence interval [CI] 1.02-1.13) and 1.06 (95% CI 1.01-1.11) among individuals with a high genetic risk (above the upper tertile of the PRS) for substance misuse and depression, respectively, compared with individuals with a low genetic risk (below the lower tertile). Slightly higher ORs were noted for severe COVID-19, and similar result patterns were obtained in analyses based on publicly available GWAS summary statistics. CONCLUSIONS: Our findings suggest a potential role of genetic factors in the observed phenotypic association between psychiatric disorders and COVID-19. Our data underscore the need for increased medical surveillance for this vulnerable population during the COVID-19 pandemic.

COVID-19 related outcomes among individuals with neurodegenerative diseases: a cohort analysis in the UK biobank.

BACKGROUND: An increased susceptibility to COVID-19 has been suggested for individuals with neurodegenerative diseases, but data are scarce from longitudinal studies. METHODS: In this community-based cohort study, we included 96,275 participants of the UK Biobank who had available SARS-CoV-2 test results in Public Health England. Of these, 2617 had a clinical diagnosis of neurodegenerative diseases in the UK Biobank inpatient hospital data before the outbreak of COVID-19 (defined as January 31st, 2020), while the remaining participants constituted the reference group. We then followed both groups from January 31st, 2020 to June 14th, 2021 for ascertainment of COVID-19 outcomes, including any COVID-19, inpatient care for COVID-19, and COVID-19 related death. Logistic regression was applied to estimate the association between neurogenerative disease and risks of COVID-19 outcomes, adjusted for multiple confounders and somatic comorbidities. RESULTS: We observed an elevated risk of COVID-19 outcomes among individuals with a neurodegenerative disease compared with the reference group, corresponding to a fully adjusted odds ratio of 2.47 (95%CI 2.25-2.71) for any COVID-19, 2.18 (95%CI 1.94-2.45) for inpatient COVID-19, and 3.67 (95%CI 3.11-4.34) for COVID-19 related death. Among individuals with a positive test result for SARS-CoV-2, individuals with neurodegenerative diseases had also a higher risk of COVID-19 related death than others (fully adjusted odds ratio 2.08; 95%CI 1.71-2.53). CONCLUSION: Among UK Biobank participants who received at least one test for SARS-CoV-2, a pre-existing diagnosis of neurodegenerative disease was associated with a subsequently increased risk of COVID-19, especially COVID-19 related death.

COVID-19 and risk of subsequent life-threatening secondary infections: a matched cohort study in UK Biobank.

BACKGROUND: With the increasing number of people infected with and recovered from coronavirus disease 2019 (COVID-19), the extent of major health consequences of COVID-19 is unclear, including risks of severe secondary infections. METHODS: Based on 445,845 UK Biobank participants registered in England, we conducted a matched cohort study where 5151 individuals with a positive test result or hospitalized with a diagnosis of COVID-19 were included in the exposed group. We then randomly selected up to 10 matched individuals without COVID-19 diagnosis for each exposed individual (n = 51,402). The life-threatening secondary infections were defined as diagnoses of severe secondary infections with high mortality rates (i.e., sepsis, endocarditis, and central nervous system infections) from the UK Biobank inpatient hospital data, or deaths from these infections from mortality data. The follow-up period was limited to 3 months after the initial COVID-19 diagnosis. Using a similar study design, we additionally constructed a matched cohort where exposed individuals were diagnosed with seasonal influenza from either inpatient hospital or primary care data between 2010 and 2019 (6169 exposed and 61,555 unexposed individuals). After controlling for multiple confounders, Cox models were used to estimate hazard ratios (HRs) of life-threatening secondary infections after COVID-19 or seasonal influenza. RESULTS: In the matched cohort for COVID-19, 50.22% of participants were male, and the median age at the index date was 66 years. During a median follow-up of 12.71 weeks, the incidence rate of life-threatening secondary infections was 2.23 (123/55.15) and 0.25 (151/600.55) per 1000 person-weeks for all patients with COVID-19 and their matched individuals, respectively, which corresponded to a fully adjusted HR of 8.19 (95% confidence interval [CI] 6.33-10.59). The corresponding HR of life-threatening secondary infections among all patients with seasonal influenza diagnosis was 4.50, 95% CI 3.34-6.08 (p for difference < 0.01). Also, elevated HRs were observed among hospitalized individuals for life-threatening secondary infections following hospital discharge, both in the COVID-19 (HR = 6.28 [95% CI 4.05-9.75]) and seasonal influenza (6.01 [95% CI 3.53-10.26], p for difference = 0.902) cohorts. CONCLUSION: COVID-19 patients have increased subsequent risks of life-threatening secondary infections, to an equal extent or beyond risk elevations observed for patients with seasonal influenza.

The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID-19): A well-mixed SEIR model analysis.

A novel coronavirus pneumonia, first identified in Wuhan City and referred to as COVID-19 by the World Health Organization, has been quickly spreading to other cities and countries. To control the epidemic, the Chinese government mandated a quarantine of the Wuhan city on January 23, 2020. To explore the effectiveness of the quarantine of the Wuhan city against this epidemic, transmission dynamics of COVID-19 have been estimated. A well-mixed "susceptible exposed infectious recovered" (SEIR) compartmental model was employed to describe the dynamics of the COVID-19 epidemic based on epidemiological characteristics of individuals, clinical progression of COVID-19, and quarantine intervention measures of the authority. Considering infected individuals as contagious during the latency period, the well-mixed SEIR model fitting results based on the assumed contact rate of latent individuals are within 6-18, which represented the possible impact of quarantine and isolation interventions on disease infections, whereas other parameter were suppose as unchanged under the current intervention. The present study shows that, by reducing the contact rate of latent individuals, interventions such as quarantine and isolation can effectively reduce the potential peak number of COVID-19 infections and delay the time of peak infection.

Clinical and Autoimmune Characteristics of Severe and Critical Cases of COVID-19.

In this study we report on the clinical and autoimmune characteristics of severe and critical novel coronavirus pneumonia caused by severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2). The clinical, autoimmune, and laboratory characteristics of 21 patients who had laboratory-confirmed severe and critical cases of coronavirus disease 2019 (COVID-19) from the intensive care unit of the Huangshi Central Hospital, Hubei Province, China, were investigated. A total of 21 patients (13 men and 8 women), including 8 (38.1%) severe cases and 13 (61.9%) critical cases, were enrolled. Cough (90.5%) and fever (81.0%) were the dominant symptoms, and most patients (76.2%) had at least one coexisting disorder on admission. The most common characteristics on chest computed tomography were ground-glass opacity (100%) and bilateral patchy shadowing (76.2%). The most common findings on laboratory measurement were lymphocytopenia (85.7%) and elevated levels of C-reactive protein (94.7%) and interleukin-6 (89.5%). The prevalence of anti-52 kDa SSA/Ro antibody, anti-60 kDa SSA/Ro antibody, and antinuclear antibody was 20%, 25%, and 50%, respectively. We also retrospectively analyzed the clinical and laboratory data from 21 severe and critical cases of COVID-19. Autoimmune phenomena exist in COVID-19 subjects, and the present results provide the rationale for a strategy of preventing immune dysfunction and optimal immunosuppressive therapy.