Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study.

BACKGROUND: Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This study aimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals. METHODS: In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set. FINDINGS: Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission. INTERPRETATION: These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection. FUNDING: Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

Elevated BMI reduces the humoral response to SARS-CoV-2 infection.

Objective: Class III obesity (body mass index [BMI] ≥ 40 kg m-2) significantly impairs the immune response to SARS-CoV-2 vaccination. However, the effect of an elevated BMI (≥ 25 kg m-2) on humoral immunity to SARS-CoV-2 infection and COVID-19 vaccination remains unclear. Methods: We collected blood samples from people who recovered from SARS-CoV-2 infection approximately 3 and 13 months of post-infection (noting that these individuals were not exposed to SARS-CoV-2 or vaccinated in the interim). We also collected blood samples from people approximately 5 months of post-second dose COVID-19 vaccination (the majority of whom did not have a prior SARS-CoV-2 infection). We measured their humoral responses to SARS-CoV-2, grouping individuals based on a BMI greater or less than 25 kg m-2. Results: Here, we show that an increased BMI (≥ 25 kg m-2), when accounting for age and sex differences, is associated with reduced antibody responses after SARS-CoV-2 infection. At 3 months of post-infection, an elevated BMI was associated with reduced antibody titres. At 13 months of post-infection, an elevated BMI was associated with reduced antibody avidity and a reduced percentage of spike-positive B cells. In contrast, no significant association was noted between a BMI ≥ 25 kg m-2 and humoral immunity to SARS-CoV-2 at 5 months of post-secondary vaccination. Conclusions: Taken together, these data showed that elevated BMI is associated with an impaired humoral immune response to SARS-CoV-2 infection. The impairment of infection-induced immunity in individuals with a BMI ≥ 25 kg m-2 suggests an added impetus for vaccination rather than relying on infection-induced immunity.

Multicenter study of mortality in achondroplasia.

Sudden death and higher mortality are recognized in achondroplasia, with acute brainstem compression, a common cause of mortality in children <4 years and cardiovascular deaths being more prevalent in adults. Although, changes in clinical management have improved survival, mortality is still higher than in the general population. The aim of this multicenter clinic-based study was to assess the rate and causes of mortality in patients seen in clinic since 1986. Information was ascertained for achondroplasia patients clinically assessed in four skeletal dysplasia clinics. Data was sent to the National Death Index to identify vital status and cause of death. Standardized mortality rates (SMR) were calculated based on U.S. populations from 1975, 1995, and 2000. Eight hundred fifty-five patients were identified, contributing 12,117 person-years and a total of 12 deaths. One case died in infancy. In the 1-4 year age group, which had the highest age-adjusted SMR, three out of five deaths were because of cerebrovascular/cardiovascular events. Half the deaths in ages 5 through 24 were because of accidental events, including motor vehicle accidents. Decreased mortality in children with achondroplasia was noted, particularly in younger age groups. This improvement in childhood survival is outpaced by improved survival in the general population. Causes of death in these patients have shifted over the last 30 years, with fewer sudden death and deaths because of pneumonia or hydrocephalus countered by more cardiovascular or cerebrovascular and accidental deaths. Clinicians should be aware of the apparent increased risk of vehicular accidents and counsel patients accordingly.